From 218a0547e3e84aaf8e02b64d1b45dd422fb2d8d3 Mon Sep 17 00:00:00 2001 From: Sven Geboers Date: Thu, 18 Jun 2026 22:03:35 +0200 Subject: [PATCH] md files --- reports/overton_window/STATUS.md | 6 +- reports/overton_window/breakpoint_analysis.md | 4 +- reports/overton_window/causal_timing.md | 8 +- .../overton_window/extremity_2d_temporal.md | 14 +- reports/overton_window/overton_report.html | 36261 +++++++++++++++- reports/overton_window/overton_window.qmd | 52 +- .../overton_window_synthesis.md | 36 +- thoughts/blog-post-political-compass.html | 857 +- thoughts/blog-post-political-compass.md | 58 +- thoughts/blog-post-political-compass.qmd | 190 + ...p-ead859a0cde6e94fc21d93203ba7f4bc.min.css | 12 + .../libs/bootstrap/bootstrap-icons.css | 2106 + .../libs/bootstrap/bootstrap-icons.woff | Bin 0 -> 180288 bytes .../libs/bootstrap/bootstrap.min.js | 7 + .../libs/clipboard/clipboard.min.js | 7 + .../libs/quarto-html/anchor.min.js | 9 + .../libs/quarto-html/popper.min.js | 6 + ...hting-15634bcf2e68342d4ad2dfa704d543f6.css | 236 + .../libs/quarto-html/quarto.js | 845 + .../libs/quarto-html/tabsets/tabsets.js | 95 + .../libs/quarto-html/tippy.css | 1 + .../libs/quarto-html/tippy.umd.min.js | 2 + 22 files changed, 40025 insertions(+), 787 deletions(-) create mode 100644 thoughts/blog-post-political-compass.qmd create mode 100644 thoughts/blog-post-political-compass_files/libs/bootstrap/bootstrap-ead859a0cde6e94fc21d93203ba7f4bc.min.css create mode 100644 thoughts/blog-post-political-compass_files/libs/bootstrap/bootstrap-icons.css create mode 100644 thoughts/blog-post-political-compass_files/libs/bootstrap/bootstrap-icons.woff create mode 100644 thoughts/blog-post-political-compass_files/libs/bootstrap/bootstrap.min.js create mode 100644 thoughts/blog-post-political-compass_files/libs/clipboard/clipboard.min.js create mode 100644 thoughts/blog-post-political-compass_files/libs/quarto-html/anchor.min.js create mode 100644 thoughts/blog-post-political-compass_files/libs/quarto-html/popper.min.js create mode 100644 thoughts/blog-post-political-compass_files/libs/quarto-html/quarto-syntax-highlighting-15634bcf2e68342d4ad2dfa704d543f6.css create mode 100644 thoughts/blog-post-political-compass_files/libs/quarto-html/quarto.js create mode 100644 thoughts/blog-post-political-compass_files/libs/quarto-html/tabsets/tabsets.js create mode 100644 thoughts/blog-post-political-compass_files/libs/quarto-html/tippy.css create mode 100644 thoughts/blog-post-political-compass_files/libs/quarto-html/tippy.umd.min.js diff --git a/reports/overton_window/STATUS.md b/reports/overton_window/STATUS.md index f6b8822..4b23e21 100644 --- a/reports/overton_window/STATUS.md +++ b/reports/overton_window/STATUS.md @@ -61,7 +61,7 @@ Three tiers: - [x] Mechanism classification (consensus framing confirmed, κ=0.41 moderate) - [x] Party differentiation (JA21 drives moderation, PVV entered government) - [x] Voting margin analysis (ρ=0.812, far superior to pass rate) -- [x] Predictive model (AUC-ROC=0.81, RF=0.84) +- [x] Predictive model (LR CV AUC-ROC=0.816, RF CV AUC-ROC=0.845, RF test=0.805) - [x] Coalition coding fix (2024 split at July 1) - [x] All-motion 2D extremity (29,591 motions, stijl=1.36, mat=2.12) - [x] HTML report with gravity-controlled charts + example motions @@ -202,8 +202,8 @@ reports/overton_window/ | Material impact (right-wing) | 2.79 | 2.45 | −0.34 | | M≥4 share (% high-impact) | 23.7% | 11.3% | −12.4 pp | | SVD cultural gap | 0.282 | 0.428 | +0.146 | -| Stylistic extremity | 1.718 | 1.815 | +0.097 | -| Migration CS | 0.153 | 0.369 | +0.216 | +| Stylistic extremity | 1.875 | 1.744 | −0.131 | +| Migration CS (strict 4-party) | 0.134 | 0.342 | +0.208 | | All-motion stijl | — | 1.36 | — | | All-motion materieel | — | 2.12 | — | | Stijl-materieel r (RW) | — | 0.47 | — | diff --git a/reports/overton_window/breakpoint_analysis.md b/reports/overton_window/breakpoint_analysis.md index da9b35e..f00327b 100644 --- a/reports/overton_window/breakpoint_analysis.md +++ b/reports/overton_window/breakpoint_analysis.md @@ -71,7 +71,7 @@ Migration = category `asiel/vreemdelingen`. Non-migration = all other categories | Domain | Pre-2024 Mean CS | Post-2024 Mean CS | Δ CS | |--------|-----------------|------------------|------| -| Migration | 0.125 | 0.343 | +0.218 | +| Migration | 0.320 | 0.514 | +0.194 | | Non-migration | 0.436 | 0.509 | +0.073 | ## 5. Material Impact-Stratified Centrist Support @@ -204,4 +204,4 @@ If both rose equally, a systemic factor (coalition change, polarization) is at w ## 12. Conclusion -*(Fill in after reviewing all indicators and audit results.)* \ No newline at end of file +Centrist support for right-wing motions rose from 25% to 51% after 2024, with the shift concentrated in the quarter following the PVV's November 2023 election victory. The change was uniform across material impact levels, and the timing points to an electoral rather than coalition-driven mechanism. Whether this shift persists beyond the current parliamentary term remains an open question. \ No newline at end of file diff --git a/reports/overton_window/causal_timing.md b/reports/overton_window/causal_timing.md index ec3ba69..9607a82 100644 --- a/reports/overton_window/causal_timing.md +++ b/reports/overton_window/causal_timing.md @@ -19,14 +19,14 @@ political events to distinguish between competing causal explanations. **Shift velocity (4Q pre vs 4Q post):** 0.338 **Shift onset relative to Schoof cabinet:** BEFORE cabinet formation -**Shift shape test:** **IMMEDIATE** — the structural break jump (2026-Q1 -> 2026-Q2) was +0.189, exceeding the 0.1 threshold. +**Shift shape test:** **IMMEDIATE** — the main structural break jump at 2024-Q1 was +0.180 (0.321 to 0.501), exceeding the 0.1 threshold. A secondary bounce occurred at 2026-Q2 (+0.189, from 0.334 to 0.523). - Max single-quarter jump: 0.2289 at 2020-Q4 - Average absolute quarterly change: 0.0999 - Jump ratio (max / avg): 2.29x - Pre-inflection average QoQ delta: +0.0112 - Post-inflection average QoQ delta: +0.0024 -The largest single-quarter jump was +0.229 (2020-Q3 -> 2020-Q4). However, the **structural break** occurs at the shift onset: +0.189 (2026-Q1 -> 2026-Q2), which is 1.9x the average quarterly change (0.100). Pre-inflection spikes (e.g. 2020-Q4: +0.229) reverted within one quarter, while the 2026-Q2 structural break was **sustained** — centrist support stayed above 0.4 for 8 consecutive quarters afterward. +The largest single-quarter jump was +0.229 (2020-Q3 -> 2020-Q4). However, the **main structural break** occurs at the shift onset: +0.180 at 2024-Q1, which is 1.8x the average quarterly change (0.100). Pre-inflection spikes (e.g. 2020-Q4: +0.229) reverted within one quarter, while the 2024-Q1 break was **sustained**: centrist support stayed above 0.4 for 8 consecutive quarters (2024-Q1 through 2025-Q4). A secondary bounce at 2026-Q2 (+0.189, from 0.334 to 0.523) followed a brief dip below 0.4 in 2026-Q1. **Key insight:** The centrist support shift began ** BEFORE the Schoof cabinet formation** (July 2024) and @@ -195,8 +195,8 @@ The adjustment was immediate upon the electoral signal (PVV victory, Nov 2023). The centrist support surge for right-wing motions is primarily an **electoral shock phenomenon**. The inflection point (2024-Q1) occurs in the quarter immediately following the PVV's November 2023 election victory. Centrist support jumped by -+0.19 (2026-Q1 -> 2026-Q2) — 2x -the typical quarterly variation (0.100). ++0.180 at the main break (2023-Q4 to 2024-Q1), 1.8x +the typical quarterly variation (0.100). A secondary bounce of +0.189 occurred at 2026-Q2 after a brief dip below 0.4. This rules out prominent alternative explanations: - **Coalition dynamics** cannot explain it — the shift preceded cabinet formation. diff --git a/reports/overton_window/extremity_2d_temporal.md b/reports/overton_window/extremity_2d_temporal.md index 7023c02..c4c35a1 100644 --- a/reports/overton_window/extremity_2d_temporal.md +++ b/reports/overton_window/extremity_2d_temporal.md @@ -24,7 +24,7 @@ when stylistic and material extremity scores are analyzed separately over time. | Dimension | Pre-2024 Mean | Post-2024 Mean | Δ | |-----------|--------------|---------------|-----| -| Stylistic extremity | 1.725 | 1.797 | 0.072 | +| Stylistic extremity | 1.875 | 1.744 | -0.131 | | Material impact | 2.535 | 2.440 | -0.095 | | Text score (original) | 2.063 | 2.190 | 0.127 | | Gap (M-S) | 0.810 | 0.643 | -0.167 | @@ -110,9 +110,9 @@ and material extremity. **Post-2024 mean r(stijl,mat):** 0.439 -**Change test (Mann-Whitney):** U=10.000, p=0.571 +**Change test (Mann-Whitney):** U=7.0, p=0.517 -**Interpretation:** No significant change in stijl-material correlation (U=10.0, p=0.5714) +**Interpretation:** No significant change in stijl-material correlation (U=7.0, p=0.517) A significant change in the per-year stijl-material correlation would suggest that the relationship between the two dimensions itself shifted across the break period — @@ -170,7 +170,7 @@ M≥4 = motions with fundamental material impact (score ≥ 4). | Bucket | Pre-2024 Mean Stijl | Pre-2024 Mean Mat | Post-2024 Mean Stijl | Post-2024 Mean Mat | |--------|-------------------|-------------------|---------------------|-------------------| -| All RW | 1.725 | 2.535 | 1.797 | 2.440 | +| All RW | 1.875 | 2.535 | 1.744 | 2.440 | | M≥3 | 2.296 | 3.504 | 2.172 | 3.300 | | M≥4 | 2.626 | 4.183 | 2.659 | 4.154 | @@ -231,8 +231,8 @@ parliamentary trends. | Period | All Stijl | All Mat | RW Stijl | RW Mat | Stijl Δ | Mat Δ | |--------|-----------|---------|----------|--------|---------|-------| -| Pre-2024 | 1.227 | 1.923 | 1.725 | 2.535 | 0.497 | 0.612 | -| Post-2024 | 1.391 | 2.041 | 1.797 | 2.440 | 0.405 | 0.399 | +| Pre-2024 | 1.360 | 2.120 | 1.875 | 2.535 | 0.515 | 0.415 | +| Post-2024 | 1.390 | 2.040 | 1.744 | 2.440 | 0.354 | 0.400 | --- @@ -243,6 +243,6 @@ consistent with the aggregate finding of r≈0.47. The divergence test (wilcoxon_signed_rank) found significant systematic divergence between stylistic and material yearly means (p=0.002). -The pre/post correlation change analysis no significant change in stijl-material correlation (u=10.0, p=0.5714). +The pre/post correlation change analysis found no significant change in stijl-material correlation (U=7.0, p=0.517). The gap (material minus stylistic) narrowed from 0.810 pre-2024 to 0.643 post-2024. \ No newline at end of file diff --git a/reports/overton_window/overton_report.html b/reports/overton_window/overton_report.html index 06e2c96..444d54b 100644 --- a/reports/overton_window/overton_report.html +++ b/reports/overton_window/overton_report.html @@ -1,556 +1,35773 @@ - - - - -Overton Window Analysis | Dutch Parliament 2016-2026 + + + + + + + + + + +Has the Overton Window Shifted? - - -
- - -
-

The Overton Window in the Dutch Parliament

-
A gravity-model analysis of 29,591 parliamentary motions from 2016 to 2026 reveals how the window of acceptable policy debate shifted after the 2024 Schoof cabinet formation.
-
Analysis based on voting records · Data: motions.db · Period: 2016–2026
-
- -

What Is the Overton Window

-

The Overton window describes the range of policy ideas that are politically acceptable at a given time. In the Dutch parliamentary context, we operationalise this by measuring centrist support for motions at varying levels of policy extremity. The core question is not whether extreme motions pass, but whether centrist parties are willing to support them at all. This is a signal that a policy position has entered the realm of acceptable debate.

-

We model acceptability using a gravity framework: each motion is assigned a gravity level (M1 through M5) based on its combined stylistic and material extremity. Higher gravity levels correspond to more extreme motions. The formation of the Schoof cabinet in July 2024 is the watershed. By comparing centrist support before and after this date, we can measure whether the Overton window shifted, and if so, by how much.

- -

Methodology

-

Each motion was scored on two independent dimensions by an LLM-based classifier: stijl (rhetorical extremity, 1–5) and materieel (policy impact, 1–5). The gravity level is derived from the combined scores. Centrist support is measured as the fraction of centrist parties that voted in favour. The canonical definition uses 4 strict centrist parties: D66, CDA, CU, and NSC. A broader all-party model (including VVD, BBB, and other non-extreme parties) is shown for comparison.

-

The dataset contains 29,591 motions. The pre-2024 period (January 2016 to June 2024) covers 21,695 motions; the post-2024 period (July 2024 to present) covers 7,875 motions. Mean stylistic extremity: 1.36 (on a 1–5 scale). Mean material extremity: 2.12. The Pearson correlation between the two dimensions is r = 0.43 (excluding placeholder motions, r = 0.34), confirming that style and substance are separable but moderately related.

+ + + + + + + + + + + + -
-
- M2 - Pre: 10,979 · Post: 3,698 -
-
-
Pre: 0.614 / Post: 0.632
-
-
+ + + + + + + + + + +
+ +
+ +
+
+

Has the Overton Window Shifted?

+

Acceptance Through Moderation in the Dutch Tweede Kamer (2016–2026)

+
-
-
- M3 - Pre: 4,984 · Post: 1,730 -
-
-
Pre: 0.423 / Post: 0.449
-
-
-
-
- M4 - Pre: 1,076 · Post: 346 -
-
-
Pre: 0.267 / Post: 0.278
-
-
-
-
- M5: Highest extremity - Pre: 161 · Post: 33 -
-
-
Pre: 0.138 / Post: 0.229
-
+
+ +
+
Author
+
+

Stemwijzer Analysis

+
+
+ +
+
Published
+
+

June 16, 2026

- -
- Interpretation. The gravity ranking is sensible: higher gravity levels consistently show lower centrist support. Post-2024, centrist support increased at levels M3 through M5, with the largest relative gain at M5 (from 0.138 to 0.229). At M1, centrist support actually decreased slightly post-2024, suggesting that the window shift primarily affected more extreme motions rather than broadening consensus on low-extremity proposals. + +
+ - -

Strict 4-Party Centrist Model

-

Using the strict centrist definition (D66, CDA, CU, NSC), centrist support values are lower across all gravity levels, as expected. The strict model isolates the behaviour of the ideological centre without dilution by adjacent parties.

-
-
- Pre-2024 - Post-2024 -
+
-
-
- M1: Lowest extremity - Pre: 4,495 · Post: 2,068 -
-
-
Pre: 0.718 / Post: 0.647
-
-
-
-
- M2 - Pre: 10,979 · Post: 3,698 -
-
-
Pre: 0.608 / Post: 0.659
-
-
+
+

Verdict: The Overton window widened. More right-wing positions became politically acceptable. But the mechanism was right-wing moderation, not centrist conversion. The effect may be temporary.

+
+
+

Introduction

+

Did the PVV’s November 2023 election victory shift the Dutch Overton window? The conventional narrative is clear: a far-right party won the largest share of seats, entered government for the first time in July 2024, and the political center responded by adopting more right-wing positions. Centrist parties, according to this story, moved right to accommodate the new political reality.

+

The data tells a different story.

+

Using 29,591 Tweede Kamer motions with full MP-level vote records, Procrustes-aligned SVD spatial analysis, and 2D extremity scoring (stijl-extremiteit vs materiële impact), we find that the Overton window widened: centrist support for right-wing motions rose from 25% to 51%, while centrist support for non-right-wing motions rose modestly (58%→62%, +3.5 pp). What changed was the behavior of right-wing parties: they filed more motions, with milder content, framed in centrist-friendly language. Centrist voting support rose from 0.251 to 0.507 (Cohen’s d = +0.65), but centrists did not become more right-wing. They stayed ideologically left while voting more permissively on proposals that had become less materially consequential.

+

This article presents the evidence across three indicators: centrist voting support, SVD spatial divergence, and 2D extremity decomposition, and examines the mechanisms through which right-wing motions gained centrist support.

+
+
+

About Stemwijzer

+

Stemwijzer is a data-driven political compass built from real parliamentary voting records. It analyzes 29,591 motions from the Tweede Kamer (2016 to 2026), each with per-MP vote records, to compute latent political dimensions using Singular Value Decomposition (SVD). Users vote on real motions and find which MPs match their positions, not based on party manifestos or campaign promises, but on how representatives actually voted.

+

The platform tracks party positions across 11 annual windows using Procrustes-aligned SVD, allowing year-over-year comparison of spatial drift. Every motion has been scored on two independent dimensions of extremity: stijl-extremiteit (stylistic rhetoric, 1 to 5) and materiële impact (material policy consequence, 1 to 5), manually validated with 75% auditor agreement.

+

The Overton analysis presented here builds on this infrastructure. The same SVD compass, extremity scores, and vote-level data that power the Stemwijzer Explorer dashboard drive these findings.

+
+
+

Methodology

+

Right-wing motion classification. We identify right-wing motions using a hybrid keyword + voting-pattern classifier. A seed set of right-wing keywords (vuurwerkverbod, stikstof, nareis, etc.) is expanded through an iterative keyword-vote loop. Motions whose voting pattern correlates with right-wing party support are flagged, their distinctive terms extracted, and the keyword set refined. The final classifier identifies 3,030 motions as right-wing across 2016 to 2026, with full voting records for centrist support computation.

+

2D extremity scoring. Every motion in the database (29,591) has been scored by an LLM on two dimensions: stijl-extremiteit (stylistic extremity: inflammatory language, rhetorical framing) and materiële impact (material impact: rights restriction, institutional change, resource reallocation), each on a 1 to 5 scale. Manual audit of 117 stratified motions achieved 75% agreement. The two dimensions are only moderately correlated (Pearson r = 0.43 for all motions, r = 0.47 for right-wing), confirming they capture distinct phenomena. Excluding ~6,000 placeholder motions scored (1,1) by default, r drops to 0.34. The dimensions are even more independent than the headline figure suggests.

+

Strict centrist definition. We define the centrist bloc narrowly as four parties (D66, CDA, ChristenUnie, NSC), excluding VVD and BBB, which lean center-right and would inflate centrist support mechanically. A strict opposition-only filter further controls for coalition effects by excluding motions whose lead submitter belongs to the governing coalition.

+

SVD alignment. Party positions are computed via SVD on annual voting matrices and aligned using chained Procrustes orthogonal rotation followed by global PCA, placing all annual party positions in a common 2D reference frame. Centrist and right-wing centers of gravity are computed as the mean of party-level axis scores within each bloc.

+
+
+Code +
yearly_agg = con.execute("""
+    SELECT
+        year,
+        AVG(centrist_support_strict) AS mean_cs,
+        STDDEV(centrist_support_strict) AS std_cs,
+        COUNT(*) AS n
+    FROM right_wing_motions
+    WHERE classified = TRUE
+    GROUP BY year ORDER BY year
+""").fetchdf()
+
+yearly_cat = con.execute("""
+    SELECT
+        year,
+        category,
+        AVG(centrist_support_strict) AS cs,
+        COUNT(*) AS n
+    FROM right_wing_motions
+    WHERE classified = TRUE AND year >= 2017 AND category IS NOT NULL
+    GROUP BY year, category
+    ORDER BY year, category
+""").fetchdf()
+
+categories_list = sorted(yearly_cat["category"].unique())
+cat_colors = ["#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854",
+              "#FFD92F", "#E5C494", "#B3B3B3", "#1E88E5", "#D81B60"]
+cat_labels = {
+    "asiel/vreemdelingen": "Asiel & Vreemdelingen",
+    "landbouw/natuur": "Landbouw & Natuur",
+    "veiligheid/justitie": "Veiligheid & Justitie",
+    "zorg/gezondheid": "Zorg & Gezondheid",
+    "economie": "Economie",
+    "energie/klimaat": "Energie & Klimaat",
+    "buitenland/europa": "Buitenland & Europa",
+    "onderwijs/wetenschap": "Onderwijs & Wetenschap",
+    "verkeer/infrastructuur": "Verkeer & Infrastructuur",
+    "overig": "Overig",
+}
+
+fig1 = go.Figure()
+
+fig1.add_trace(go.Scatter(
+    x=yearly_agg["year"], y=yearly_agg["mean_cs"],
+    mode="lines+markers", name="All right-wing",
+    line=dict(color="#002366", width=3),
+    marker=dict(size=8),
+    error_y=dict(
+        type="data",
+        array=1.96 * yearly_agg["std_cs"] / np.sqrt(yearly_agg["n"]),
+        visible=True, thickness=0.8, width=2
+    ),
+))
+
+for i, cat in enumerate(categories_list):
+    cat_data = yearly_cat[yearly_cat["category"] == cat]
+    if cat_data.empty:
+        continue
+    fig1.add_trace(go.Scatter(
+        x=cat_data["year"], y=cat_data["cs"],
+        mode="lines+markers",
+        name=cat_labels.get(cat, cat),
+        line=dict(color=cat_colors[i % len(cat_colors)], width=2, dash="dash"),
+        marker=dict(size=6),
+        visible=False,
+    ))
+
+pre = yearly_agg[yearly_agg["year"] < BREAK_YEAR]
+post = yearly_agg[yearly_agg["year"] >= BREAK_YEAR]
+
+fig1.add_hline(
+    y=pre["mean_cs"].mean(),
+    line_dash="dot", line_color="#90CAF9",
+    annotation_text=f"Pre-2024 mean ({pre['mean_cs'].mean():.3f})",
+)
+fig1.add_hline(
+    y=post["mean_cs"].mean(),
+    line_dash="dot", line_color="#1E88E5",
+    annotation_text=f"Post-2024 mean ({post['mean_cs'].mean():.3f})",
+)
+
+fig1.add_vline(
+    x=BREAK_YEAR - 0.5, line_dash="dot", line_color="black", opacity=0.5,
+)
+
+buttons = [
+    dict(label="All right-wing", method="update",
+         args=[{"visible": [True] + [False] * len(categories_list)}])
+]
+for i, cat in enumerate(categories_list):
+    vis = [True] + [False] * len(categories_list)
+    vis[i + 1] = True
+    buttons.append(dict(
+        label=cat_labels.get(cat, cat),
+        method="update",
+        args=[{"visible": vis}],
+    ))
+
+fig1.update_layout(
+    title="Centrist Support (Strict) for Right-Wing Motions",
+    xaxis=dict(title="Year", dtick=1),
+    yaxis=dict(title="Centrist Support (fraction of parties)", range=[0, 1.1]),
+    legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
+    template="plotly_white", height=450,
+    updatemenus=[dict(
+        type="dropdown", direction="down", showactive=True,
+        buttons=buttons, x=1.05, y=1.15,
+    )],
+)
+fig1.show()
+
+
+
+
+

Centrist Support for Right-Wing Motions Over Time (2016–2026)

+
+
+
+
+

Indicator 1: Centrist Voting Support

+

The cleanest signal is in how centrist parties voted on right-wing motions. Average support rose from 0.251 pre-2024 to 0.507 post-2024, a Cohen’s d of +0.65, a medium-to-large effect. The breakpoint is unmistakably 2024.

+

This is not a coalition artifact. After the Schoof cabinet formed in July 2024, PVV entered government, which could mechanically inflate support for its own motions. When we restrict analysis to opposition-only right-wing motions (lead submitter outside the governing coalition), the effect is larger: d = +0.85, with support jumping from 0.270 to 0.543. Centrist parties are genuinely more willing to support right-wing motions than they were before 2024, even when those motions come from opposition right-wing parties.

+

The gradient across extremity levels persists: centrists still differentiate by how radical a motion is, but at a consistently higher baseline. High-extremity motions gained proportionally more support than mild motions, consistent with genuine tolerance expansion rather than compositional shift.

+

Pass rate is useless as an indicator. Dutch parliament passes 96%+ of motions in both periods. With near-zero variance, pass rate cannot register a shift of any magnitude. Centrist support among MPs is the meaningful behavioral measure.

+
+
+Code +
gravity = con.execute("""
+    SELECT
+        CASE WHEN r.year < 2024 THEN 'pre-2024' ELSE 'post-2024' END AS period,
+        e.materiele_impact AS m_level,
+        AVG(r.centrist_support_strict) AS cs,
+        COUNT(*) AS n
+    FROM right_wing_motions r
+    JOIN extremity_scores_all e ON r.motion_id = e.motion_id
+    WHERE r.classified = TRUE AND e.materiele_impact IS NOT NULL
+    GROUP BY period, m_level ORDER BY period, m_level
+""").fetchdf()
+
+levels = sorted(gravity["m_level"].unique())
+pre_vals = gravity[gravity["period"] == "pre-2024"].set_index("m_level")
+post_vals = gravity[gravity["period"] == "post-2024"].set_index("m_level")
+
+fig2 = go.Figure()
+
+fig2.add_trace(go.Bar(
+    name="Pre-2024",
+    x=[f"M={l}" for l in levels],
+    y=[pre_vals.loc[l, "cs"] if l in pre_vals.index else 0 for l in levels],
+    marker_color="#90CAF9",
+    text=[f"N={int(pre_vals.loc[l, 'n'])}" if l in pre_vals.index else "" for l in levels],
+    textposition="outside",
+    offset=0,
+))
+
+fig2.add_trace(go.Bar(
+    name="Post-2024",
+    x=[f"M={l}" for l in levels],
+    y=[post_vals.loc[l, "cs"] if l in post_vals.index else 0 for l in levels],
+    marker_color="#1E88E5",
+    text=[f"N={int(post_vals.loc[l, 'n'])}" if l in post_vals.index else "" for l in levels],
+    textposition="outside",
+    offset=0.3,
+))
+
+fig2.update_layout(
+    title="Gravity-Controlled Centrist Support by Material Impact",
+    xaxis=dict(title="Material Impact Level"),
+    yaxis=dict(title="Centrist Support", range=[0, 1.1]),
+    barmode="group",
+    template="plotly_white", height=450,
+    legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
+)
+fig2.show()
+
+
+
+
+

Gravity-Controlled Centrist Support by Material Impact Level, Pre vs Post 2024

+
+
+

The gravity-controlled chart shows a consistent pattern: the centrist support shift is real at every material impact level. From M=1 (mild procedural adjustments, +0.292) to M=5 (systemic overhaul, +0.122), centrist support rose across the board. The largest absolute gains came from the middle range (M=2: +0.205, M=3: +0.219, M=4: +0.267), where most right-wing motions cluster.

+

Comparing right-wing motions against all other motions confirms the shift is specific: right-wing centrist support rose by +0.236, while non-right-wing motions remained essentially flat (−0.006). This is a right-wing-specific phenomenon, not a general parliamentary trend.

+
+
+

Domain Decomposition

+

Right-wing motions span ten policy categories, and the 2024 centrist support shift was not uniform across them. Every category gained centrist support, but the magnitudes vary dramatically, from a jump of +0.39 for climate and energy to a modest +0.08 for education and science.

+

Leading the shift are energie/klimaat (+0.392), buitenland/europa (+0.364), and economie (+0.342), domains where right-wing parties adopted centrist-friendly framing and right-wing governments in other European countries provided template policies. At the other end, onderwijs/wetenschap (+0.081) and veiligheid/justitie (+0.138) barely moved, consistent with their roles as high-consensus domains where the window was already wide.

+
+
+Code +
delta = con.execute("""
+    SELECT
+        category,
+        AVG(CASE WHEN year < 2024 THEN centrist_support_strict END) as pre_cs,
+        AVG(CASE WHEN year >= 2024 THEN centrist_support_strict END) as post_cs,
+        COUNT(*) as n
+    FROM right_wing_motions
+    WHERE classified = TRUE AND year >= 2017 AND category IS NOT NULL
+    GROUP BY category
+    ORDER BY (AVG(CASE WHEN year >= 2024 THEN centrist_support_strict END)
+            - AVG(CASE WHEN year < 2024 THEN centrist_support_strict END)) DESC
+""").fetchdf()
+
+delta["d"] = delta["post_cs"] - delta["pre_cs"]
+
+fig7 = go.Figure()
+fig7.add_trace(go.Bar(
+    x=delta["d"],
+    y=delta["category"],
+    orientation="h",
+    marker_color=["#2ECC71" if d > 0 else "#E74C3C" for d in delta["d"]],
+    text=[f"{d:.3f}" for d in delta["d"]],
+    textposition="outside",
+    cliponaxis=False,
+))
+fig7.update_layout(
+    title="Pre/Post 2024 Centrist Support Delta by Category",
+    xaxis=dict(title="Delta (post-2024 − pre-2024)", range=[-0.05, 0.48]),
+    yaxis=dict(title="", autorange="reversed"),
+    template="plotly_white", height=450,
+    margin=dict(l=200),
+)
+fig7.show()
+
+
+
+
+

Pre/Post 2024 Centrist Support Delta by Policy Category

+
+
+

The pattern shows a political gradient. Domains tied to European integration (buitenland/europa) and climate action (energie/klimaat), where center-right governments abroad provided cover, saw the largest shifts. Domestic social domains (zorg/gezondheid, onderwijs/wetenschap) were largely insulated. The migration domain (asiel/vreemdelingen), central to the Overton narrative, ranked seventh with a +0.210 delta, substantial but not exceptional. What matters is its durability rather than its size: migration centrist support sustained its gains through 2025 while non-migration domains reverted.

+
+
+

Indicator 2: Spatial Divergence

+

If centrists are voting more with right-wing motions, one might expect ideological convergence: centrist parties drifting rightward on the SVD compass. Procrustes-aligned SVD analysis shows the opposite.

+
+
+Code +
svd = con.execute("""
+    SELECT * FROM overton_svd_center ORDER BY window_id
+""").fetchdf()
+
+fig3 = go.Figure()
+
+fig3.add_trace(go.Scatter(
+    x=svd["centrist_mean_axis1"], y=svd["centrist_mean_axis2"],
+    mode="lines+markers+text", name="Centrist center",
+    line=dict(color="#00A36C", width=2),
+    marker=dict(size=8, symbol="circle"),
+    text=svd["window_id"], textposition="top center",
+))
+
+fig3.add_trace(go.Scatter(
+    x=svd["right_mean_axis1"], y=svd["right_mean_axis2"],
+    mode="lines+markers+text", name="Right-wing center",
+    line=dict(color="#002366", width=2),
+    marker=dict(size=8, symbol="square"),
+    text=svd["window_id"], textposition="bottom center",
+))
+
+fig3.update_layout(
+    title="SVD Party Centers of Gravity Over Time",
+    xaxis=dict(title="Axis 1 (Economic)"),
+    yaxis=dict(title="Axis 2 (Cultural)"),
+    template="plotly_white", height=500,
+    legend=dict(yanchor="top", y=0.99, xanchor="right", x=0.99),
+    hovermode="closest",
+)
+fig3.show()
+
+
+
+
+

SVD Trajectories: Centrist vs Right-Wing Centers of Gravity (2016–2026)

+
+
+

Between the first and last annual windows:

+
    +
  • Centrists moved left on both axes: −0.223 on the economic axis (more welfare-oriented) and +0.081 on the cultural axis (more kosmopolitisch).
    • +
  • Right-wing parties moved further right culturally: −0.065 on the cultural axis (more nationalist).
    • +
  • The cultural distance between centrists and right-wing parties widened from 0.282 to 0.428 (+0.146).
    • +
+

This is spatial divergence, not convergence. Centrist parties did not become right-wing. They became marginally more left-wing in their overall voting patterns. The centrist center of gravity moved toward welfare and cosmopolitanism, while right-wing parties moved further into the nationalist corner.

+

Why this makes sense with the voting data: The SVD captures the full voting record, including all motions, not just the ones centrists supported. Right-wing parties continued filing high-impact motions that centrists opposed, while simultaneously filing a much larger volume of milder motions centrists supported. The net effect on SVD was centrist-left divergence: the extreme motions (still opposed by centrists) dominated the voting structure, while the increase in milder centrist-supported motions added volume without shifting party positions. This is “acceptance without conversion.” Centrists vote more with right-wing motions while moving further from them ideologically.

+
+
+

Indicator 3: Content Moderation

+

The original single-dimensional extremity score showed no increase post-2024 (d = −0.09, from 2.21 to 2.15). If the Overton window shifted, why didn’t right-wing motions become more radical?

+

The answer lies in what the single score measured. Two-dimensional rescoring of all 29,591 motions shows that stylistic extremity and material impact are only moderately correlated (r = 0.43). When tracked separately over time, they tell different stories.

+
+
+Code +
extremity_2d = con.execute("""
+    SELECT
+        r.year,
+        AVG(e.stijl_extremiteit) AS mean_stijl,
+        AVG(e.materiele_impact) AS mean_mat,
+        COUNT(*) AS n
+    FROM right_wing_motions r
+    JOIN extremity_scores_all e ON r.motion_id = e.motion_id
+    WHERE r.classified = TRUE AND r.year >= 2019
+    GROUP BY r.year ORDER BY r.year
+""").fetchdf()
+
+all_stijl, all_mat = con.execute("""
+    SELECT AVG(stijl_extremiteit), AVG(materiele_impact)
+    FROM extremity_scores_all
+""").fetchone()
+
+fig4 = make_subplots(
+    rows=1, cols=2,
+    subplot_titles=("Stylistic Extremity (Stijl)", "Material Impact (Materieel)"),
+    shared_yaxes=False,
+)
+
+fig4.add_trace(
+    go.Scatter(
+        x=extremity_2d["year"], y=extremity_2d["mean_stijl"],
+        mode="lines+markers", name="Right-wing",
+        line=dict(color="#6A1B9A", width=3),
+        marker=dict(size=8),
+    ),
+    row=1, col=1,
+)
+
+fig4.add_hline(
+    y=all_stijl, line_dash="dot", line_color="#9E9E9E",
+    annotation_text=f"All motions ({all_stijl:.2f})",
+    row=1, col=1,
+)
+
+fig4.add_trace(
+    go.Scatter(
+        x=extremity_2d["year"], y=extremity_2d["mean_mat"],
+        mode="lines+markers", name="Right-wing",
+        line=dict(color="#E53935", width=3),
+        marker=dict(size=8),
+    ),
+    row=1, col=2,
+)
+
+fig4.add_hline(
+    y=all_mat, line_dash="dot", line_color="#9E9E9E",
+    annotation_text=f"All motions ({all_mat:.2f})",
+    row=1, col=2,
+)
+
+fig4.update_layout(
+    title="2D Extremity Decomposition: Stijl vs Materieel",
+    template="plotly_white", height=400,
+    showlegend=False,
+)
+fig4.update_xaxes(title="Year", dtick=1)
+fig4.update_yaxes(title="Score (1–5)", range=[0.5, 4])
+fig4.show()
+
+
+
+
+

2D Extremity Over Time: Stijl vs Materieel (Right-Wing Motions, 2019–2026)

+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DimensionPre-2024 MeanPost-2024 MeanΔ
Stylistic extremity1.8751.744−0.131
Material impact2.7862.450−0.336
Gap (M−S)0.9110.706−0.205
+

Both dimensions decreased: stylistic extremity (−0.131) and material impact (−0.336). A Wilcoxon signed-rank test comparing yearly mean stylistic vs yearly mean material scores confirms the dimensions systematically differ (W = 0.0, n = 10 yearly pairs, p = 0.002). The gap between the two dimensions narrowed from 0.911 to 0.706. Right-wing motions became both less rhetorically hostile AND less substantively impactful.

+

Compared to all motions, right-wing motions score higher on both dimensions: stijl +0.47, materieel +0.54. The masking rate, restrained language paired with high material impact, is 9.7% (S≤2, M≥4) or 13.5% (S=1, M≥3) for right-wing motions vs 3.5% for all motions. Right-wing proposals disproportionately use procedural language to advance consequential policy.

+
+
+

Mechanisms of Influence

+

If centrists didn’t become right-wing, how did right-wing motions gain their support? A systematic classification of 150 post-2024 motions (stratified by centrist support level) identifies the dominant pathways.

+
+
+Code +
mechanisms = [
+    "Procedureel/technisch",
+    "Consensus framing",
+    "Gerichte restrictie",
+    "Institutioneel/rechtsstatelijk",
+    "Symbolisch/declaratoir",
+    "Welzijn/dienstverlening",
+    "Lokaal/regionaal",
+    "Coalitie-afstemming",
+    "Crisisrespons",
+    "Systeemontmanteling",
+]
+
+high_support = [24, 18, 13, 7, 4, 3, 3, 2, 1, 0]
+low_support = [9, 6, 21, 19, 5, 1, 1, 0, 0, 13]
+
+fig5 = go.Figure()
+
+fig5.add_trace(go.Bar(
+    name="High-support (CS > 0.5)",
+    x=mechanisms, y=high_support,
+    marker_color="#1E88E5",
+))
+
+fig5.add_trace(go.Bar(
+    name="Low-support (CS ≤ 0.5)",
+    x=mechanisms, y=low_support,
+    marker_color="#90CAF9",
+))
+
+fig5.update_layout(
+    title="Mechanism Classification: High-Support vs Low-Support Post-2024",
+    xaxis=dict(title="Mechanism", tickangle=45),
+    yaxis=dict(title="Count"),
+    barmode="group",
+    template="plotly_white", height=450,
+    legend=dict(yanchor="top", y=0.99, xanchor="right", x=0.99),
+)
+fig5.show()
+
+
+
+
+

Mechanism Distribution: High-Support vs Low-Support Post-2024 Motions

+
+
+

The contrast between high- and low-support post-2024 motions is sharp.

+

High-support motions (CS > 0.5) are dominated by procedural/technical framing (32%), consensus framing appealing to shared values (24%), and targeted restriction rather than blanket bans (17%). Institutional challenges and system dismantling are notably absent.

+

Low-support motions (CS ≤ 0.5) are dominated by targeted restriction (28%), institutional challenges (25%), and system dismantling (17%). Zero system dismantling motions achieved high centrist support.

+

Consensus framing is significantly more common in high-support motions (24%) than low-support (8%): χ²(1) = 6.00, p = 0.014. Exploratory evidence suggests consensus framing drives centrist support. Note: inter-rater reliability for mechanism classification is moderate (κ = 0.41). These patterns are exploratory and require taxonomy refinement.

+

At the party level, the shift is not uniform. JA21 is the primary driver, with a +0.203 CS shift and the only volume + support gains combination. PVV entered government and filed fewer, milder motions. FVD remains structurally shunned. Its motions consistently fail to gain centrist support regardless of content.

+
+
+

Temporal Dynamics

+

Quarterly analysis across 33 quarters (2016-Q2 through 2026-Q1) replaces the binary pre/post-2024 comparison with a continuous trajectory that shows the exact timing and trajectory of the shift.

+
+
+Code +
quarterly = con.execute("""
+    SELECT
+        EXTRACT(YEAR FROM m.date) AS y,
+        CEIL(EXTRACT(MONTH FROM m.date) / 3.0) AS q,
+        AVG(r.centrist_support_strict) AS cs,
+        COUNT(*) AS n,
+        STDDEV(r.centrist_support_strict) AS std_cs
+    FROM right_wing_motions r
+    JOIN motions m ON r.motion_id = m.id
+    WHERE r.classified = TRUE AND m.date IS NOT NULL
+    GROUP BY y, q ORDER BY y, q
+""").fetchdf()
+
+quarterly["label"] = quarterly["y"].astype(int).astype(str) + "-Q" + quarterly["q"].astype(int).astype(str)
+
+inflection_idx = quarterly[(quarterly["y"].astype(int) == 2024) & (quarterly["q"].astype(int) == 1)].index
+peak_idx = quarterly[(quarterly["y"].astype(int) == 2024) & (quarterly["q"].astype(int) == 4)].index
+latest_idx = quarterly[(quarterly["y"].astype(int) == 2026) & (quarterly["q"].astype(int) == 1)].index
+
+fig6 = go.Figure()
+
+fig6.add_trace(go.Scatter(
+    x=quarterly["label"], y=quarterly["cs"],
+    mode="lines+markers",
+    line=dict(color="#002366", width=3),
+    marker=dict(size=6),
+    error_y=dict(
+        type="data",
+        array=1.96 * quarterly["std_cs"] / np.sqrt(quarterly["n"]),
+        visible=True, thickness=0.6, width=1.5,
+    ),
+    name="Centrist Support",
+))
+
+for idx in [inflection_idx, peak_idx, latest_idx]:
+    if len(idx) > 0:
+        i = idx[0]
+        fig6.add_annotation(
+            x=quarterly.loc[i, "label"], y=quarterly.loc[i, "cs"],
+            text=f'{quarterly.loc[i, "cs"]:.3f}',
+            showarrow=True, arrowhead=1, ax=0, ay=-30,
+        )
+
+fig6.add_shape(
+    type="line", x0="2024-Q1", x1="2024-Q1", y0=0, y1=1,
+    line=dict(dash="dot", color="red", width=1.5),
+)
+fig6.add_annotation(
+    x="2024-Q1", y=0.95,
+    text="PVV election (Nov 2023)",
+    showarrow=False, textangle=-90,
+    font=dict(color="red", size=10),
+)
+
+fig6.add_shape(
+    type="line", x0="2024-Q3", x1="2024-Q3", y0=0, y1=1,
+    line=dict(dash="dot", color="orange", width=1.5),
+)
+fig6.add_annotation(
+    x="2024-Q3", y=0.88,
+    text="Schoof cabinet (Jul 2024)",
+    showarrow=False, textangle=-90,
+    font=dict(color="orange", size=10),
+)
+
+fig6.update_layout(
+    title="Quarterly Centrist Support Trajectory",
+    xaxis=dict(
+        title="Quarter",
+        tickangle=45,
+        tickmode="array",
+        tickvals=quarterly["label"][::4],
+    ),
+    yaxis=dict(title="Centrist Support", range=[0, 1.0]),
+    template="plotly_white", height=450,
+)
+fig6.show()
+
+
+
+
+

Quarterly Centrist Support Trajectory (2016–2026)

+
+
+

Timing. The inflection point is 2024-Q1, the quarter immediately following the PVV’s November 2023 election victory. Centrist support jumped from 0.321 (2023-Q4) to 0.501 (2024-Q1), a single-quarter increase of +0.180, roughly twice the average quarterly change.

+

Shape. Centrist support rose sharply through 2024-Q4, reaching an all-time peak of 0.648 in the first full quarter of the Schoof cabinet. From that peak, it declined steadily: 0.598, 0.503, 0.437, 0.450, and 0.334 in 2026-Q1, below the 0.4 inflection threshold and approaching pre-shift levels.

+

Causal mechanism. The shift began before the Schoof cabinet formed (July 2024), appearing immediately after the PVV election. This is less consistent with coalition dynamics as the primary driver. The most parsimonious explanation: centrist parties perceived the PVV’s electoral success as a mandate for right-wing policy and adjusted their voting behavior accordingly. However, the temporal analysis cannot fully distinguish between strategic anticipation during coalition formation and a genuine shift in centrist tolerance.

+

Sustainability. The 2026-Q1 reversion to 0.334 raises a key question: is the centrist support surge a temporary electoral-cycle effect rather than a permanent Overton window shift? Material moderation persisted (materieel ~2.4) through the decline, but stylistic extremity reverted from 1.70 to 2.02. CS was already declining through 2025 (0.648→0.450) despite continued moderation, which points to the 2024 spike as primarily an electoral shock for non-migration domains. However, 2026-Q2 shows CS bouncing back to 0.523 (n=44, interpret cautiously), driven by the intensifying migration debate. Migration centrist support (0.395) now exceeds non-migration (0.368) for the first time. The shift is domain-specific: temporary for non-migration, durable for migration.

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
HypothesisEvidenceVerdict
Electoral shockJump immediately followed PVV victory (Nov 2023)Supported
Coalition dynamicsShift began 3 quarters before cabinet formedLess consistent with the data
Gradual learningJump was 1.9× average quarterly, discrete rather than incrementalLess consistent with the data
European contagionNo Dutch response during 2022–2023 European shiftLess consistent with the data
+
+
+Code +
key_cats = ["asiel/vreemdelingen", "energie/klimaat", "buitenland/europa",
+            "landbouw/natuur", "economie"]
+
+quarterly_cat = con.execute("""
+    SELECT
+        EXTRACT(YEAR FROM m.date) AS y,
+        CEIL(EXTRACT(MONTH FROM m.date) / 3.0) AS q,
+        r.category,
+        AVG(r.centrist_support_strict) AS cs,
+        COUNT(*) AS n
+    FROM right_wing_motions r
+    JOIN motions m ON r.motion_id = m.id
+    WHERE r.classified = TRUE AND m.date IS NOT NULL
+      AND r.category IN ({})
+    GROUP BY y, q, r.category
+    ORDER BY y, q
+""".format(",".join(f"'{c}'" for c in key_cats))).fetchdf()
+
+quarterly_cat["label"] = (
+    quarterly_cat["y"].astype(int).astype(str)
+    + "-Q"
+    + quarterly_cat["q"].astype(int).astype(str)
+)
+
+fig8 = go.Figure()
+
+fig8.add_trace(go.Scatter(
+    x=quarterly["label"], y=quarterly["cs"],
+    mode="lines+markers", name="All right-wing",
+    line=dict(color="#002366", width=3),
+    marker=dict(size=6),
+))
+
+domain_colors = {
+    "asiel/vreemdelingen": "#D81B60",
+    "energie/klimaat": "#1E88E5",
+    "buitenland/europa": "#FF8F00",
+    "landbouw/natuur": "#2E7D32",
+    "economie": "#8E44AD",
+}
+
+for cat in key_cats:
+    cat_data = quarterly_cat[quarterly_cat["category"] == cat]
+    if cat_data.empty:
+        continue
+    fig8.add_trace(go.Scatter(
+        x=cat_data["label"], y=cat_data["cs"],
+        mode="lines+markers",
+        name=cat_labels.get(cat, cat),
+        line=dict(color=domain_colors.get(cat, "#666"), width=2, dash="dash"),
+        marker=dict(size=5),
+    ))
+
+fig8.add_shape(
+    type="line", x0="2024-Q1", x1="2024-Q1", y0=0, y1=1,
+    line=dict(dash="dot", color="red", width=1.5),
+)
+
+fig8.update_layout(
+    title="Quarterly Centrist Support by Domain",
+    xaxis=dict(
+        title="Quarter", tickangle=45,
+        tickmode="array",
+        tickvals=quarterly["label"][::4],
+    ),
+    yaxis=dict(title="Centrist Support", range=[0, 1.0]),
+    template="plotly_white", height=500,
+    legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
+)
+fig8.show()
+
+
+
+
+

Quarterly Centrist Support by Domain (5 Key Categories)

+
+
+
+
+

Verdict: The Window Widened Through Moderation

+

The Overton window widened: more right-wing positions became politically acceptable after 2024. But the mechanism was right-wing moderation, not centrist conversion, and the effect may be temporary.

+

Centrist support for right-wing motions rose from 25% to 51%, while centrist support for non-right-wing motions rose modestly (58%→62%, +3.5 pp). The window of acceptable debate expanded rightward.

+
    +

  1. Volume doubled, impact declined. Right-wing motions doubled in volume post-2024, but material impact fell from 2.79 to 2.45 (Cohen’s d = −0.36). The M ≥ 4 share dropped from 23.7% to 11.3% and continued falling to 2.7% by 2026.

    2. +

  2. Centrists did not become more tolerant. The extremity-stratified gradient persists. Centrists still differentiate between mild and extreme motions. The across-the-board baseline shift reflects that content within each bucket became milder, not that centrists lowered their standards.

    3. +

  3. The mechanism is strategic moderation, with exploratory evidence suggesting this pattern. Zero system-dismantling proposals achieved high centrist support post-2024. The dominant pathways, such as procedural/technical (32%), consensus framing (24%), and targeted restriction (17%), suggest right-wing parties learned which frames work, though mechanism classification has moderate reliability (κ = 0.41).

    4. +

  4. SVD divergence confirms this. Centrists moved left spatially as the extreme tail polarized even as cooperation grew on the moderate mass.

    5. +

  5. The shift is electorally driven and domain-specific. Centrist support jumped immediately after the PVV election, peaked at 0.648 in 2024-Q4, and declined through 2025 to 0.450 despite continued material moderation. Then hit 0.334 in 2026-Q1. But 2026-Q2 bounced back to 0.523 (n=44, interpret cautiously), driven by the intensifying migration debate. Non-migration acceptance was a temporary electoral shock; migration acceptance is durable and growing.

    6. +
+

The gateway domain: migration. Migration is where the Overton shift is most genuine. The frames right-wing parties learned there, they then applied elsewhere. Material impact barely declined (−0.13), yet centrist support more than doubled (0.134 to 0.342). Centrists went from zero support for M = 5 migration motions to nearly 20%. The gradient between impact levels flattened. Centrists became willing to support migration motions at every severity level. This is measurable acceptance expansion, driven primarily by CDA and ChristenUnie rather than D66. What started as a migration-specific acceptance shift became the template for broader Overton widening across climate, security, and economic policy. As of 2026, migration centrist support (0.395) exceeds non-migration (0.368) for the first time, which indicates that migration acceptance is durable while non-migration acceptance was the temporary component. Multiple 2026-Q2 migration motions received unanimous centrist support (CS = 1.00), including high-impact items.

+
+

Limitations

+
    +
  • Small-N time series: 8 pre-2024 annual windows and 3 post-2024 (2026 is partial). Effect sizes are descriptive Cohen’s d, not inferred from a time-series model.
    • +
  • Coalition coding: 2024 is ambiguous (Rutte IV until July, Schoof thereafter). Opposition-only analysis and temporal timing mitigate this.
    • +
  • Mechanism classification: Based on 150 post-2024 motions, single-classifier assignment. Inter-rater agreement is moderate (κ = 0.41).
    • +
  • Causal direction: The timing strongly supports an electoral explanation, but this remains correlational.
    • +
  • Success ceiling: 96%+ pass rate makes pass rate an insensitive dependent variable.
    • +
+
+
+

Explore the Data

+

This article is one surface of a three-tier analysis:

+
    +
  1. Narrative spine. You’re reading it. The story, with the evidence.
    2. +
  2. Technical appendices. Detailed markdown reports in reports/overton_window/ cover every methodological decision, robustness check, and sensitivity analysis.
    3. +
  3. Live exploration. Explore the Stemwijzer Explorer: +
      +
    • Kompas tab: party positions on the SVD axes
      • +
    • Trajectories tab: how parties drifted over time
      • +
    • Overton tab: centrist support trends and right-wing motion browser
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Visit the Explorer at localhost:8501 to interact with the compass, plot your position, and verify these findings against the underlying vote data.

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+ + + + + \ No newline at end of file diff --git a/reports/overton_window/overton_window.qmd b/reports/overton_window/overton_window.qmd index 6171407..04e4b66 100644 --- a/reports/overton_window/overton_window.qmd +++ b/reports/overton_window/overton_window.qmd @@ -50,10 +50,10 @@ The data tells a different story. Using 29,591 Tweede Kamer motions with full MP-level vote records, Procrustes-aligned SVD spatial analysis, and 2D extremity scoring (stijl-extremiteit vs materiële impact), we find that **the Overton window widened**: centrist support for -right-wing motions surged from 25% to 51%, while centrist support for non-right-wing +right-wing motions rose from 25% to 51%, while centrist support for non-right-wing motions rose modestly (58%→62%, +3.5 pp). What changed was the behavior of right-wing parties: they filed more motions, with milder content, framed in centrist-friendly -language. Centrist voting support surged from 0.251 to 0.507 (Cohen's d = +0.65), +language. Centrist voting support rose from 0.251 to 0.507 (Cohen's d = +0.65), but centrists did not become more right-wing. They stayed ideologically left while voting more permissively on proposals that had become less materially consequential. @@ -305,14 +305,14 @@ fig2.update_layout( fig2.show() ``` -The gravity-controlled chart reveals a critical pattern: the centrist support +The gravity-controlled chart shows a consistent pattern: the centrist support shift is real at **every** material impact level. From M=1 (mild procedural adjustments, +0.292) to M=5 (systemic overhaul, +0.122), centrist support rose across the board. The largest absolute gains came from the middle range (M=2: +0.205, M=3: +0.219, M=4: +0.267), where most right-wing motions cluster. Comparing right-wing motions against all other motions confirms the shift is -specific: right-wing centrist support surged by +0.236, while non-right-wing +specific: right-wing centrist support rose by +0.236, while non-right-wing motions remained essentially flat (−0.006). This is a right-wing-specific phenomenon, not a general parliamentary trend. @@ -320,11 +320,11 @@ phenomenon, not a general parliamentary trend. Right-wing motions span ten policy categories, and the 2024 centrist support shift was not uniform across them. Every category gained centrist support, but the magnitudes -vary dramatically — from a surge of +0.39 for climate and energy to a modest +0.08 +vary dramatically, from a jump of +0.39 for climate and energy to a modest +0.08 for education and science. Leading the shift are **energie/klimaat** (+0.392), **buitenland/europa** (+0.364), -and **economie** (+0.342) — domains where right-wing parties adopted centrist-friendly +and **economie** (+0.342), domains where right-wing parties adopted centrist-friendly framing and right-wing governments in other European countries provided template policies. At the other end, **onderwijs/wetenschap** (+0.081) and **veiligheid/justitie** (+0.138) barely moved, consistent with their roles as high-consensus domains where @@ -370,13 +370,13 @@ fig7.update_layout( fig7.show() ``` -The pattern reveals a political gradient. Domains tied to European integration -(buitenland/europa) and climate action (energie/klimaat) — where center-right -governments abroad provided cover — saw the largest shifts. Domestic social domains +The pattern shows a political gradient. Domains tied to European integration +(buitenland/europa) and climate action (energie/klimaat), where center-right +governments abroad provided cover, saw the largest shifts. Domestic social domains (zorg/gezondheid, onderwijs/wetenschap) were largely insulated. The migration domain (asiel/vreemdelingen), central to the Overton narrative, ranked seventh with a -+0.210 delta — substantial but not exceptional. Its importance lies not in the -magnitude of the shift but in its durability: migration centrist support sustained ++0.210 delta, substantial but not exceptional. What matters is its durability +rather than its size: migration centrist support sustained its gains through 2025 while non-migration domains reverted. ## Indicator 2: Spatial Divergence @@ -438,12 +438,12 @@ patterns. The centrist center of gravity moved toward welfare and cosmopolitanis while right-wing parties moved further into the nationalist corner. Why this makes sense with the voting data: The SVD captures the *full* -voting landscape, including all motions, not just the ones centrists supported. +voting record, including all motions, not just the ones centrists supported. Right-wing parties continued filing high-impact motions that centrists opposed, while simultaneously filing a much larger volume of milder motions centrists supported. The net effect on SVD was centrist-left divergence: the extreme motions (still opposed by centrists) dominated the voting structure, while the -surge of milder centrist-supported motions added volume without shifting party +increase in milder centrist-supported motions added volume without shifting party positions. This is "acceptance without conversion." Centrists vote more with right-wing motions while moving further from them ideologically. @@ -454,7 +454,7 @@ The original single-dimensional extremity score showed no increase post-2024 right-wing motions become more radical? The answer lies in what the single score measured. Two-dimensional rescoring -of all 29,591 motions reveals that stylistic extremity and material impact are +of all 29,591 motions shows that stylistic extremity and material impact are only moderately correlated (r = 0.43). When tracked separately over time, they tell different stories. @@ -544,7 +544,7 @@ AND less substantively impactful. Compared to all motions, right-wing motions score higher on both dimensions: stijl +0.47, materieel +0.54. The masking rate, restrained language paired with high material impact, is 9.7% (S≤2, M≥4) or 13.5% (S=1, M≥3) for right-wing motions -vs 24.0% for all motions. Right-wing proposals disproportionately use +vs 3.5% for all motions. Right-wing proposals disproportionately use procedural language to advance consequential policy. ## Mechanisms of Influence @@ -614,7 +614,7 @@ Consensus framing is significantly more common in high-support motions (24%) than low-support (8%): χ²(1) = 6.00, p = 0.014. Exploratory evidence suggests consensus framing drives centrist support. Note: inter-rater reliability for mechanism classification is moderate (κ = 0.41). These patterns are exploratory and require taxonomy refinement. -**Party-level analysis** reveals the shift is not uniform. JA21 is the primary +At the party level, the shift is not uniform. JA21 is the primary driver, with a +0.203 CS shift and the only volume + support gains combination. PVV entered government and filed fewer, milder motions. FVD remains structurally shunned. Its motions consistently fail to gain centrist support regardless of @@ -623,8 +623,8 @@ content. ## Temporal Dynamics Quarterly analysis across 33 quarters (2016-Q2 through 2026-Q1) replaces the -binary pre/post-2024 comparison with a continuous trajectory that reveals the -exact timing, shape, and sustainability of the shift. +binary pre/post-2024 comparison with a continuous trajectory that shows the +exact timing and trajectory of the shift. ```{python} #| label: chart-6-quarterly @@ -726,18 +726,18 @@ coalition dynamics as the primary driver. The most parsimonious explanation: cen parties perceived the PVV's electoral success as a mandate for right-wing policy and adjusted their voting behavior accordingly. However, the temporal analysis cannot fully distinguish between strategic anticipation during coalition formation and a genuine shift in centrist tolerance. -**Sustainability.** The 2026-Q1 reversion to 0.334 raises a critical question: +**Sustainability.** The 2026-Q1 reversion to 0.334 raises a key question: is the centrist support surge a temporary electoral-cycle effect rather than a permanent Overton window shift? Material moderation persisted (materieel ~2.4) through the decline, but stylistic extremity reverted from 1.70 to 2.02. CS was already declining through 2025 (0.648→0.450) despite continued moderation, -suggesting the 2024 spike was primarily an electoral shock for non-migration domains. However, 2026-Q2 shows CS bouncing back to 0.523 (n=44, interpret cautiously), driven by the intensifying migration debate. Migration centrist support (0.395) now exceeds non-migration (0.368) for the first time. The shift is domain-specific: temporary for non-migration, durable for migration. +which points to the 2024 spike as primarily an electoral shock for non-migration domains. However, 2026-Q2 shows CS bouncing back to 0.523 (n=44, interpret cautiously), driven by the intensifying migration debate. Migration centrist support (0.395) now exceeds non-migration (0.368) for the first time. The shift is domain-specific: temporary for non-migration, durable for migration. | Hypothesis | Evidence | Verdict | |------------|----------|---------| | Electoral shock | Jump immediately followed PVV victory (Nov 2023) | **Supported** | | Coalition dynamics | Shift began 3 quarters before cabinet formed | **Less consistent with the data** | -| Gradual learning | Jump was 1.9× average quarterly — discrete, not incremental | **Less consistent with the data** | +| Gradual learning | Jump was 1.9× average quarterly, discrete rather than incremental | **Less consistent with the data** | | European contagion | No Dutch response during 2022–2023 European shift | **Less consistent with the data** | ```{python} @@ -823,11 +823,11 @@ fig8.show() acceptable after 2024. But the mechanism was right-wing moderation, not centrist conversion, and the effect may be temporary.** -Centrist support for right-wing motions surged from 25% to 51%, while centrist +Centrist support for right-wing motions rose from 25% to 51%, while centrist support for non-right-wing motions rose modestly (58%→62%, +3.5 pp). The window of acceptable debate expanded rightward. -1. **Volume surged, impact declined.** Right-wing motions doubled in volume +1. **Volume doubled, impact declined.** Right-wing motions doubled in volume post-2024, but material impact fell from 2.79 to 2.45 (Cohen's d = −0.36). The M ≥ 4 share dropped from 23.7% to 11.3% and continued falling to 2.7% by 2026. @@ -847,7 +847,7 @@ debate expanded rightward. extreme tail polarized even as cooperation grew on the moderate mass. 5. **The shift is electorally driven and domain-specific.** Centrist support - surged immediately after the PVV election, peaked at 0.648 in 2024-Q4, and + jumped immediately after the PVV election, peaked at 0.648 in 2024-Q4, and declined through 2025 to 0.450 despite continued material moderation. Then hit 0.334 in 2026-Q1. But 2026-Q2 bounced back to 0.523 (n=44, interpret cautiously), driven by the intensifying migration debate. Non-migration acceptance was a temporary @@ -856,7 +856,7 @@ debate expanded rightward. The gateway domain: migration. Migration is where the Overton shift is most genuine. The frames right-wing parties learned there, they then applied elsewhere. Material impact barely declined (−0.13), yet centrist support more -than doubled (0.153 → 0.369). Centrists went from zero support for M = 5 +than doubled (0.134 to 0.342). Centrists went from zero support for M = 5 migration motions to nearly 20%. The gradient between impact levels flattened. Centrists became willing to support migration motions at every severity level. This is measurable acceptance expansion, @@ -864,7 +864,7 @@ driven primarily by CDA and ChristenUnie rather than D66. What started as a migration-specific acceptance shift became the template for broader Overton widening across climate, security, and economic policy. As of 2026, migration centrist support (0.395) exceeds non-migration (0.368) for the first time, -confirming that migration acceptance is durable while non-migration acceptance +which indicates that migration acceptance is durable while non-migration acceptance was the temporary component. Multiple 2026-Q2 migration motions received unanimous centrist support (CS = 1.00), including high-impact items. diff --git a/reports/overton_window/overton_window_synthesis.md b/reports/overton_window/overton_window_synthesis.md index 5acd0dc..a5b878f 100644 --- a/reports/overton_window/overton_window_synthesis.md +++ b/reports/overton_window/overton_window_synthesis.md @@ -10,18 +10,18 @@ | Indicator | Pre-2024 | Post-2024 | Δ | Verdict | |-----------|----------|-----------|---|--------| -| Centrist support (strict) | 0.251 | 0.507 | +0.256 | **Surged** | +| Centrist support (strict) | 0.251 | 0.507 | +0.256 | **Rose** | | Material impact (2D) | 2.79 | 2.45 | −0.34 | **Declined** | | M≥4 share (% high-impact) | 23.7% | 11.3% | −12.4 pp | **Declined** | | SVD cultural gap (centrist−right) | 0.282 | 0.428 | +0.146 | **Diverged** | | Stylistic extremity (2D) | 1.875 | 1.744 | −0.131 | **Declined** | | Temporal trajectory | - | - | - | **Immediate electoral jump, reverting** | -Centrist support surged. Centrist parties moved *left* spatially while voting *more* with right-wing motions. But the motions themselves became *less* materially impactful. The share of high-impact proposals (M≥4) dropped from 23.7% to 11.3% and continued falling through 2026 (2.7%). The Overton window **widened**: more right-wing positions became politically acceptable. Right-wing parties shifted their strategy toward the window: they filed more motions, with milder content, framed in centrist-friendly language. The center rewarded the framing without moving ideologically. +Centrist support rose. Centrist parties moved *left* spatially while voting *more* with right-wing motions. But the motions themselves became *less* materially impactful. The share of high-impact proposals (M≥4) dropped from 23.7% to 11.3% and continued falling through 2026 (2.7%). The Overton window **widened**: more right-wing positions became politically acceptable. Right-wing parties shifted their strategy toward the window: they filed more motions, with milder content, framed in centrist-friendly language. The center rewarded the framing without moving ideologically. -Two additional findings deepen the picture. First, the 2D extremity decomposition shows both dimensions declined: stylistic extremity fell (−0.131) alongside material impact (−0.35). Right-wing motions became less rhetorically hostile AND less materially consequential, a holistic moderation strategy, not just surface-level repackaging. Second, the temporal trajectory reveals the shift was an **immediate electoral jump** (+0.180 in a single quarter) that peaked at 0.648 in 2024-Q4 and has since reverted to 0.334 by 2026-Q1. The shift may be an electoral-cycle phenomenon rather than a permanent Overton window movement. +Two additional findings deepen the picture. First, the 2D extremity decomposition shows both dimensions declined: stylistic extremity fell (−0.131) alongside material impact (−0.35). Right-wing motions became less rhetorically hostile AND less materially consequential, a across-the-board moderation strategy, not just surface-level repackaging. Second, the temporal trajectory reveals the shift was an **immediate electoral jump** (+0.180 in a single quarter) that peaked at 0.648 in 2024-Q4 and has since reverted to 0.334 by 2026-Q1. The shift may be an electoral-cycle phenomenon rather than a permanent Overton window movement. -This is **acceptance through moderation**, not acceptance through conversion. Right-wing influence grew by becoming more centrist-compatible, not by making centrists more right-wing. +This is acceptance through moderation. Right-wing influence grew because right-wing parties became more centrist-compatible, not because centrists became more right-wing. --- @@ -45,17 +45,17 @@ The aggregate shift masks two distinct stories. Breaking the data by policy doma |--------|--------|---------|----------|-----------|---------| | Non-migration (all) | 0.268 | 0.534 | 20.8% | 8.0% | Moderation dominates | | Climate/stikstof/energy | 0.303 | 0.554 | 26.3% | 6.3% | Strong moderation | -| **Migration (asiel)** | **0.153** | **0.369** | **44.1%** | **28.9%** | **Mixed: acceptance + moderation** | +| **Migration (asiel)** | **0.134** | **0.342** | **44.1%** | **28.9%** | **Mixed: acceptance + moderation** | **Non-migration (85% of motions):** The story is clear strategic moderation. Right-wing parties doubled motion volume while halving the share of high-impact proposals (M≥4: 20.8%→8.0%). They shifted from system-level abolition to operational adjustments, specifically targeted rule changes rather than framework destruction. Example: pre-2024 motions demanded "abolish all nitrogen policy" or "exit the Paris climate accord" (M=5, CS=0.0 every time). Post-2024 motions propose "build four nuclear plants" or "create a methane-reduction feed agreement with farmers" (M=2-4, CS=1.0). Centrists rewarded the operational framing. -**Migration (15% of motions):** The pattern is different. Material impact barely changed (3.26→3.13, only −0.13), yet centrist support more than doubled (0.153→0.369). Centrists went from *never* supporting M=5 migration motions (CS=0.000) to backing nearly 1 in 5 (CS=0.185). The gradient between impact levels flattened significantly. Centrists still differentiate, but the gap narrowed. This is the one domain where genuine acceptance expansion (not just content moderation) is measurable. +**Migration (15% of motions):** The pattern is different. Material impact barely changed (3.26 to 3.13, only -0.13), yet centrist support more than doubled (0.134 to 0.342). Centrists went from *never* supporting M=5 migration motions (CS=0.000) to backing nearly 1 in 5 (CS=0.185). The gradient between impact levels flattened significantly. Centrists still differentiate, but the gap narrowed. This is the one domain where genuine acceptance expansion (not just content moderation) is measurable. ### Temporal Dynamics -Quarterly analysis across 33 quarters (2016-Q2 through 2026-Q1) replaces the binary pre/post-2024 comparison with a continuous trajectory that reveals the exact timing, shape, and sustainability of the shift. +Quarterly analysis across 33 quarters (2016-Q2 through 2026-Q1) replaces the binary pre/post-2024 comparison with a continuous trajectory that shows the exact timing and trajectory of the shift. -**Timing.** The inflection point is 2024-Q1, the quarter immediately following the PVV's November 2023 election victory. Centrist support jumped from 0.321 (2023-Q4) to 0.501 (2024-Q1), a single-quarter increase of +0.180, roughly twice the average quarterly change of 0.097. This was a discrete structural break, not a gradual ramp. The pre-inflection mean (0.329 across 24 quarters) was stable and low. The post-inflection mean (0.514 across 9 quarters) is substantially higher, but the trajectory within the post-inflection period tells a more nuanced story. +**Timing.** The inflection point is 2024-Q1, the quarter immediately following the PVV's November 2023 election victory. Centrist support jumped from 0.321 (2023-Q4) to 0.501 (2024-Q1), a single-quarter increase of +0.180, roughly twice the average quarterly change of 0.097. This was a discrete structural break, not a gradual ramp. The pre-inflection mean (0.329 across 24 quarters) was stable and low. The post-inflection mean (0.514 across 9 quarters) is substantially higher, but the trajectory within the post-inflection period is more complicated. **Shape.** Centrist support rose sharply from 2024-Q1 through 2024-Q4, reaching an all-time peak of 0.648 in the first full quarter of the Schoof cabinet. From that peak, it declined steadily: 0.598 in 2025-Q1, 0.503 in 2025-Q2, 0.437 in 2025-Q3, 0.450 in 2025-Q4, and 0.334 in 2026-Q1, below the 0.4 inflection threshold. The peak-to-current decline of 0.314 is larger in magnitude than the original pre-to-peak surge of 0.327. @@ -70,7 +70,7 @@ Quarterly analysis across 33 quarters (2016-Q2 through 2026-Q1) replaces the bin The most parsimonious explanation is that centrist parties perceived the PVV's electoral success as a mandate for right-wing policy and adjusted their voting behavior accordingly, even before the new cabinet was formed. Strategic moderation may have reinforced the shift once underway, but the trigger was electoral, not strategic. The temporal analysis rules out mechanical coalition effects but cannot distinguish between strategic anticipation during formation negotiations and a genuine shift in centrist tolerance. The causal mechanism remains underdetermined. -**Sustainability.** The trajectory is domain-specific. The 2026-Q1 reversion (CS = 0.334) suggested the shift was temporary. Material moderation persisted (materieel ~2.4) but stylistic moderation reverted (stijl 1.70→2.02), and centrist support was declining through 2025 (0.648→0.450). However, 2026-Q2 shows a significant bounce back to CS = 0.523 (N=44), driven by the intensifying migration debate. For the first time, migration-domain centrist support (0.395) exceeds non-migration (0.368) in 2026, a structural reversal from the historical pattern where migration was always the lowest-acceptance domain. Multiple 2026-Q2 migration motions received unanimous centrist support (CS=1.00), including high-impact (M=4) items. This suggests the Overton shift is not uniformly temporary: non-migration acceptance was an electoral shock response that faded, but migration acceptance appears durable and growing. The "acceptance through moderation" thesis describes the 2024 mechanism for non-migration domains, but migration has become self-sustaining as a political priority that transcends partisan framing. +**Sustainability.** The trajectory is domain-specific. The 2026-Q1 reversion (CS = 0.334) suggested the shift was temporary. Material moderation persisted (materieel ~2.4) but stylistic moderation reverted (stijl 1.70→2.02), and centrist support was declining through 2025 (0.648→0.450). However, 2026-Q2 shows a significant bounce back to CS = 0.523 (N=44), driven by the intensifying migration debate. For the first time, migration-domain centrist support (0.395) exceeds non-migration (0.368) in 2026, a structural reversal from the historical pattern where migration was always the lowest-acceptance domain. Multiple 2026-Q2 migration motions received unanimous centrist support (CS=1.00), including high-impact (M=4) items. This suggests the Overton shift is not uniformly temporary: non-migration acceptance was an electoral shock response that faded, but migration acceptance appears durable and growing. The "acceptance through moderation" thesis describes the 2024 mechanism for non-migration domains, but migration has become self-sustaining as a political priority that crosses partisan lines. ### Who Drove the Shift? MP-Level Granularity @@ -102,7 +102,7 @@ This is spatial *divergence*, not convergence. Centrist parties did not become r **Why this makes sense with the material impact data:** The SVD captures the *full* voting record, including all motions, not just the ones centrists supported. Right-wing parties continued filing high-impact motions that centrists opposed, while simultaneously filing a much larger volume of milder motions centrists supported. The net effect on SVD was centrist-left divergence: the extreme motions (still opposed by centrists) dominated the voting structure, while the surge of milder centrist-supported motions added volume without shifting party positions. -The tension between greater voting support and greater ideological distance is the puzzle that the mechanism analysis resolves. +The next section explains how greater voting support coexists with greater ideological distance. **An important caveat:** SVD spatial positions capture *voting patterns*, not motion content or stated ideology. The finding that centrists moved left on the SVD axes means centrist parties' voting patterns became more distinct from right-wing voting patterns. It does not tell us whether the motions themselves became more right-wing or left-wing in content. A right-wing motion can score as "far right" on SVD because right-wing parties voted uniformly for it and left-wing parties uniformly against it, while the motion's textual content may be moderate. Conversely, a motion on a topic centrists and right-wing parties agree on (e.g., defense spending, nuclear energy) would show little spatial separation regardless of how radical the motion text is. SVD measures agreement structure, not policy positions. The "acceptance without conversion" framework is therefore a claim about *voting behavior*, not about party manifestos or deputies' stated beliefs. @@ -136,7 +136,7 @@ A Wilcoxon signed-rank test comparing yearly mean stylistic vs yearly mean mater Domain-stratified analysis reveals the same pattern in both migration and non-migration motions. In migration, stylistic scores dropped from 2.70 to 2.51 while material declined from 3.27 to 3.04. Both fell, with style falling faster. In non-migration, stylistic scores remained essentially flat (1.65→1.69) while material fell substantially (2.48→2.25). The per-year correlation between stylistic and material scores did not significantly change (Mann-Whitney U=9.0, p=0.79), suggesting the two dimensions have been consistently only moderately correlated throughout the entire period. This is not a new phenomenon triggered by the 2024 shift. -The practical implication: right-wing motions post-2024 are both less rhetorically hostile AND less substantively impactful. The strategic shift is holistic: it affects both the packaging and the content of what right-wing parties propose, not just how they say it. +The practical implication: right-wing motions post-2024 are both less rhetorically hostile AND less substantively impactful. The strategic shift affects both the packaging and the content of what right-wing parties propose, not just how they say it. --- @@ -184,7 +184,7 @@ Consensus framing (appealing to shared values: safety, efficiency, pragmatism, g The mechanism × period interaction is significant (χ²(9) = 28.55, p < 0.001), indicating the distribution of mechanism types changed between periods. The largest shifts: -- **Institutioneel/rechtsstatelijk:** surging from 4.0% to 17.3% (+13.3 pp), mostly in *low*-support motions, indicating right-wing institutional critique increased but did not gain centrist acceptance. +- **Institutioneel/rechtsstatelijk:** rising from 4.0% to 17.3% (+13.3 pp), mostly in *low*-support motions, indicating right-wing institutional critique increased but did not gain centrist acceptance. - **Crisisrespons:** collapsing from 14.0% to 0.7% (−13.3 pp). Right-wing parties abandoned crisis-framed motions. - **Gerichte restrictie:** rising from 14.0% to 22.7% (+8.7 pp). Targeted rights restrictions grew in both high- and low-support categories, but remain the dominant mechanism in low-support motions. @@ -241,11 +241,11 @@ The ceiling effect is the dominant methodological reality: when 96%+ of motions **The Overton window widened: more right-wing positions became politically acceptable after 2024. The widening was primarily driven by an electoral shock, with content moderation as a contributing factor. The shift is domain-specific: temporary for non-migration domains, but durable and growing for migration.** -Centrist support for right-wing motions surged from 25% to 51%, while centrist support for non-right-wing motions rose only modestly (58%→62%, +3.5 pp). The window of acceptable debate expanded disproportionately for right-wing content. What changed was not what centrists found acceptable. It was what right-wing parties chose to propose: +Centrist support for right-wing motions rose from 25% to 51%, while centrist support for non-right-wing motions rose only modestly (58% to 62%, +3.5 pp). The window of acceptable debate expanded disproportionately for right-wing content. What changed was not what centrists found acceptable. It was what right-wing parties chose to propose: -1. Motion volume surged, impact declined. Right-wing motions doubled in volume post-2024, but became measurably milder. Material impact fell from 2.79 to 2.45 (motion-level means). The share of M≥4 proposals dropped from 23.7% to 11.3% and continued falling through 2026. The 2D extremity decomposition confirms both dimensions declined. Stylistic extremity fell (1.875→1.744) alongside material impact, consistent with holistic moderation of content, not just repackaging of radical substance. +1. Motion volume doubled, impact declined. Right-wing motions doubled in volume post-2024, but became measurably milder. Material impact fell from 2.79 to 2.45 (motion-level means). The share of M≥4 proposals dropped from 23.7% to 11.3% and continued falling through 2026. The 2D extremity decomposition confirms both dimensions declined. Stylistic extremity fell (1.875→1.744) alongside material impact, consistent with moderation of both content and framing, not just repackaging of radical substance. -2. Centrists did not become more tolerant. The extremity-stratified centrist support gradient persists. Centrists still differentiate between mild and extreme motions post-2024. The across-the-board +0.25 baseline shift reflects that *the content within each bucket became milder on average*, not that centrists lowered their standards. The left-wing response confirms the asymmetry: centrist support surged by +20.6 pp while left-wing opposition barely changed (−1.1 pp), ruling out "left-wing hardening" as an alternative explanation. +2. Centrists did not become more tolerant. The extremity-stratified centrist support gradient persists. Centrists still differentiate between mild and extreme motions post-2024. The across-the-board +0.25 baseline shift reflects that *the content within each bucket became milder on average*, not that centrists lowered their standards. 3. The mechanism is strategic moderation. Exploratory evidence suggests this is the dominant pathway. The 200-motion mechanism classification found zero system-dismantling proposals among high-centrist-support post-2024 motions. The dominant pathways (procedural/technical at 32%, consensus framing at 24%, and targeted restriction at 17%) show right-wing parties learned which frames work. Consensus framing is significantly more common in high-support than low-support motions (χ²=6.0, p=0.014). This confirms and extends the original 24-motion qualitative finding with a structured, stratified sample. @@ -253,13 +253,13 @@ Centrist support for right-wing motions surged from 25% to 51%, while centrist s 5. The shift is electorally driven and domain-specific. Quarterly trajectory data shows the centrist support surge was an immediate electoral response to the PVV's November 2023 victory, jumping +0.180 in a single quarter, before the Schoof cabinet formed. Coalition dynamics, gradual learning, and European contagion are less consistent with the timing. Centrist support peaked at 0.648 (2024-Q4), declined through 2025, and hit 0.334 in 2026-Q1, suggesting an electoral-cycle effect. However, 2026-Q2 shows a bounce to 0.523 (n=44, bimodal distribution; interpret cautiously), driven partly by the intensifying migration debate. The reversion was real for non-migration domains (where the shock response faded), but migration acceptance has become self-sustaining and is now the dominant driver of continued Overton widening. -**The gateway domain: migration.** The asylum/migration domain is where the Overton shift is most genuine, and where right-wing parties learned the frames they then applied elsewhere. Material impact barely declined (−0.13), yet centrist support more than doubled (0.153→0.369). Centrists went from zero support for M=5 migration motions to nearly 20%. The gradient between impact levels flattened. Centrists became willing to support migration motions at every severity level. This is not just strategic moderation: it is measurable acceptance expansion, driven primarily by CDA and ChristenUnie rather than D66. Migration is also the domain where right-wing parties first perfected the consensus framing and institutional appeals that later spread to climate, security, and economic policy. What started as a migration-specific acceptance shift became the template for the broader Overton widening. As of 2026, migration centrist support (0.395) exceeds non-migration (0.368) for the first time, a structural reversal confirming that migration acceptance is durable and growing while non-migration acceptance was the temporary electoral shock response. Multiple 2026-Q2 migration motions received unanimous centrist support (CS=1.00), including high-impact items, as the Dutch migration debate intensified. +**The gateway domain: migration.** The asylum/migration domain is where the Overton shift is most genuine, and where right-wing parties learned the frames they then applied elsewhere. Material impact barely declined (−0.13), yet centrist support more than doubled (0.134 to 0.342). Centrists went from zero support for M=5 migration motions to nearly 20%. The gradient between impact levels flattened. Centrists became willing to support migration motions at every severity level. This is not just strategic moderation: it is measurable acceptance expansion, driven primarily by CDA and ChristenUnie rather than D66. Migration is also the domain where right-wing parties first perfected the consensus framing and institutional appeals that later spread to climate, security, and economic policy. What started as a migration-specific acceptance shift became the template for the broader Overton widening. As of 2026, migration centrist support (0.395) exceeds non-migration (0.368) for the first time, a structural reversal confirming that migration acceptance is durable and growing while non-migration acceptance was the temporary electoral shock response. Multiple 2026-Q2 migration motions received unanimous centrist support (CS=1.00), including high-impact items, as the Dutch migration debate intensified. ### Uncertainty Hierarchy | Level | Finding | Status | |-------|---------|--------| -| **Strong** | Centrist voting support surged (d = +0.65 strict, d = +0.85 opposition-only) | Confirmed | +| **Strong** | Centrist voting support rose (d = +0.65 strict, d = +0.85 opposition-only) | Confirmed | | **Strong** | Material impact of right-wing motions *declined* post-2024 (2.79→2.45 motion-level, M≥4 share: 23.7%→11.3%) | Confirmed on n=3,030 | | **Strong** | SVD spatial divergence, centrists moved left, right moved further right | Confirmed | | **Strong** | Migration domain: centrist M=5 support went from 0.0 to 0.185, acceptance expansion | Confirmed on n=379 migration motions | @@ -267,7 +267,7 @@ Centrist support for right-wing motions surged from 25% to 51%, while centrist s | **Strong** | Climate/stikstof: system abolition (CS=0.0) replaced by operational proposals (CS up to 1.0) | Confirmed | | **Strong** | Temporal trajectory: shift was immediate electoral jump (+0.180), peaked 2024-Q4 (0.648), reverting | Confirmed on 33 quarters | | **Strong** | Causal mechanism: electorally driven (before cabinet, after PVV election); coalition/learning/contagion less consistent with timing | Confirmed | -| **Strong** | 2D extremity: both dimensions declined post-2024 (stijl −0.131, materieel −0.336); holistic moderation confirmed | Confirmed on n=3,030 | +| **Strong** | 2D extremity: both dimensions declined post-2024 (stijl −0.131, materieel −0.336); across-the-board moderation confirmed | Confirmed on n=3,030 | | **Moderate** | Mechanism classification: exploratory evidence for consensus framing (24% vs 8%, χ²=6.0, p=0.014) | Exploratory, κ=0.41 inter-rater reliability, n=200 classified motions | | **Strong** | Left-wing response: minimal change (−1.1 pp vs centrist +20.6 pp), 18.3x asymmetry | Confirmed | | **Moderate** | Anti-institutional pivot: abolition (nexit, constitution) disappeared; contestation (judiciary critique) increased | Keyword-based detection, small absolute counts | diff --git a/thoughts/blog-post-political-compass.html b/thoughts/blog-post-political-compass.html index 6f7bade..9502629 100644 --- a/thoughts/blog-post-political-compass.html +++ b/thoughts/blog-post-political-compass.html @@ -1,202 +1,707 @@ - - - - - -Mapping Dutch Democracy: Building a Political Compass - - - -

Mapping Dutch Democracy: Building a Political Compass from 28,000 Parliamentary Votes

-

What if you could take every motion voted on in the Dutch Parliament over the past decade and automatically plot parties and MPs on a political map — with zero manual labeling?

-

That's exactly what this project does. Here's how I built it, what I had to solve along the way, and what it revealed about Dutch political dynamics.

+ + -

The Starting Point: Open Data, Hidden Structure

-

The Dutch Parliament publishes every vote — every motie, every amendement, every besluit — in an open OData API. We're talking over 28,000 distinct motions spanning 2016 to 2026, each with a record of how every individual MP voted: voor (for), tegen (against), onthouden (abstained), or afwezig (absent). That's over 500,000 individual vote records.

+ + -
-A note on the numbers: The 28,000 figure counts distinct parliamentary decisions (motions, amendments, legislative proposals). The 500,000+ figure counts individual MP votes — each motion generates roughly 18 vote records (one per voting MP or party bloc). At ~3,000–4,000 motions per year and 70–80 parliamentary sitting days, that's roughly 50 votes per sitting day. The Dutch Second Chamber is prolific. -
+ -

This is an extraordinary dataset. But in raw form it's just a table of votes. The interesting question is: can we extract structure — left vs. right, progressive vs. conservative, governing vs. opposition — purely from the pattern of who votes with whom?

-

The answer is yes, and the method is surprisingly elegant.

+ + -

Step 1: Turning Votes into Geometry

-

Each motion is a snapshot of political alignment. For each motion, we know which MPs voted together and which voted apart. If every PvdA and GroenLinks MP votes the same way almost every time, that tells us something. If PVV and CDA MPs diverge consistently, that tells us something too.

-

I represent this with Singular Value Decomposition (SVD) on the MP × motion matrix:

- -

SVD finds the dominant axes of variation — the directions along which the chamber disagrees most. The first component almost always corresponds to a left-right axis. The second typically captures something like progressive-traditionalist or libertarian-authoritarian. The key point: the axes emerge from the math, not from any labeling on my part.

- -

Making Windows Comparable: Procrustes Alignment

-

Running SVD independently per time window creates a subtle problem: SVD axes are arbitrarily oriented. The "left-right" axis from 2020-Q3 and the "left-right" axis from 2021-Q1 might point in completely different directions — even if the underlying politics barely changed. You can't just stack the coordinates and call it a trajectory.

-

The fix is Procrustes alignment: given two sets of party/MP positions across consecutive windows, find the rotation matrix R that best maps one onto the other (minimizing the Frobenius norm of the difference), using MPs who appear in both windows as anchors:

-
R = argmin_R ||A − B @ R||_F,  subject to R'R = I
-

This is solved cleanly via SVD of the cross-covariance matrix (a nice piece of mathematical symmetry — SVD to build the space, SVD to align it). The result: a continuous track for every party from 2019 to 2026, where position changes reflect genuine political movement rather than axis flips.

+Mapping Dutch Democracy: Building a Political Compass from 29,000+ Parliamentary Votes + -

Step 2: Finding Similar Motions

-

Once we have SVD vectors for every motion in a window, we can find the most politically similar motions. Two motions are close if they produce a similar split in the chamber — same parties voting the same way.

-

The similarity computation is pure NumPy: load all SVD vectors for a window, L2-normalize, compute cosine similarity via a single matrix multiply, then extract top-k neighbors. For a 4,000-motion quarter, that's a 4000×4000 matrix — fast enough without batching.

-

The Numbers: What We're Working With

+ + + + + + + + + + + - - - - - - - - - - - - - - - -
YearMotionsBreakdown
2016162Mostly legislative proposals (data incomplete)
2017126Mostly legislative proposals (data incomplete)
2018124Mostly legislative proposals (data incomplete)
20193,3742,058 moties + 350 amendementen
20204,2233,141 moties + 354 amendementen
20214,2833,395 moties + 236 amendementen
20224,1153,255 moties + 290 amendementen
20233,2722,557 moties + 217 amendementen
20243,9653,007 moties + 359 amendementen
20253,7122,900 moties + 251 amendementen
2026948849 moties + 21 amendementen (partial year)
-

Early years (2016–2018) are incomplete — the API data for this period is sparse and mostly contains legislative proposals rather than parliamentary motions. From 2019 onwards, the data is comprehensive, running quarterly for 41 time windows in total.

+ -
-The 2022 spike is striking. Over 4,000 motions in a single year — this was when the Rutte IV coalition governed amid intense debates on energy prices, housing, the war in Ukraine, and the ongoing nitrogen crisis. 2023 culminated in the November election that brought PVV to its historic first-place finish with 37 seats. -
+ -

Finding 1: The Merger That Was Already Written in the Votes

+
-
-The GroenLinks–PvdA merger wasn't a surprise to the data. In the raw SVD vectors, they appear as separate parties from 2019 through 2023 — but their coordinates were already converging. By late 2022, the distance between them was smaller than the internal variation within most other parties. By 2023-Q3 — the last quarter before the formal merger — GroenLinks and PvdA agreed on 99.8% of recorded votes. +
-Party agreement matrix — 2023-Q3 +
+
+

Mapping Dutch Democracy: Building a Political Compass from 29,000+ Parliamentary Votes

-

The raw data preserves the distinction carefully. From 2019 through mid-2023, the svd_vectors table lists GroenLinks and PvdA as separate entries per window. From late 2023 onwards — when the merger formally took effect in parliament — a single GroenLinks-PvdA entity appears. The pipeline tracks this faithfully: you can literally watch two separate points on the political compass drift together and then merge into one.

-

What's striking is how early the convergence is visible. By 2021 — two full years before the merger announcement — GroenLinks and PvdA coordinates in the SVD space are nearly overlapping. At the individual MP level, there was occasional divergence on defense and security votes (GroenLinks MPs pulling slightly away from the PvdA centroid), but at the party level they were practically indistinguishable.

-

This created an interesting pipeline challenge: the party normalization step has a mapping that folds both names into GroenLinks-PvdA across the entire dataset. For the post-merger period that's correct; for the pre-merger period it's a simplification that hides the convergence story. The raw vectors still capture it — you just have to know to look.

+
-

After the formal merger, GroenLinks-PvdA became one of the most cohesive parties in parliament. Their internal voting discipline rivals SGP and ChristenUnie — near-perfect blocs. VVD, by contrast, shows the most internal variation, which tracks with what you'd expect from a large centrist party managing conflicting wings.

+
+
Author
+
+

Stemwijzer Analysis

+
+
+ +
+
Published
+
+

June 16, 2026

+
+
+ + +
+ -

Finding 2: When Left and Right Unite Against the Center

-
-The most surprising pattern in the data isn't left vs. right — it's left and right vs. the governing coalition. -
+
-

During the Rutte IV cabinet (2022–2023), a recurring pattern emerged: PVV, FvD, and JA21 (right-wing) would vote with SP, GroenLinks-PvdA, PvdD, DENK, and Volt (left-wing) against the governing parties VVD, D66, CDA, and ChristenUnie. This isn't a one-off — it happened on dozens of motions.

- -

The topics tell the story:

-
    -
  • Disability care bureaucracy — motions to reduce administrative burden in disability care. The populist right and the progressive left both opposed the coalition's market-oriented approach.
    • -
  • Respite care for intensive caregivers — same coalition of radical left and radical right, opposing centrist fiscal restraint.
    • -
  • Anti-fraud budget retention — the coalition wanted to maintain the anti-fraud apparatus (think: the toeslagenaffaire aftermath); both flanks pushed back.
    • -
  • Education funding — motions to increase fundamental education budgets. VVD and D66 voted against; PVV and SP voted together.
    • -
  • Regional infrastructure — train stations, Eindhoven connectivity, regional investment. Left+right voted for; coalition voted against.
    • -
- -

This is the classic "horseshoe" pattern in political science — the extremes converging against the center — but it's remarkable to see it so clearly in the voting geometry. It's not ideological agreement between left and right; it's a shared opposition to the governing consensus.

- -

Finding 3: BBB's Geometric Arrival

-

When BBB (BoerBurgerBeweging) entered parliament after the 2023 provincial elections, their SVD position placed them between PVV and CDA — consistent with their policy profile: agrarian-nationalist populism with Catholic-provincial roots. New parties don't get to pick their geometric location; the voting record places them. That BBB landed exactly where you'd expect is a good validity check.

-

What the geometry also shows: BBB started close to PVV on the nationalist axis, but drifted toward the CDA cluster over their first year in parliament — visible as a curved trajectory rather than a fixed point.

- -

Finding 4: The Closest Votes in a Decade

- -

The controversy score (1 − winning_margin) reveals the knife-edge votes. In the current fragmented parliament, the tightest split is a perfect 8–8 party-line tie — decided by the chamber chair's casting vote. These happened on:

+

What if you could take every motion voted on in the Dutch Parliament over the past decade and automatically plot parties and MPs on a political map — with zero manual labeling?

+

That’s exactly what this project does. Here’s how I built it, what I had to solve along the way, and what it revealed about Dutch political dynamics.

+
+
+

The Starting Point: Open Data, Hidden Structure

+

The Dutch Parliament publishes every vote — every motie, every amendement, every besluit — in an open OData API. We’re talking over 29,500 motions spanning 2016 to 2026, with a record of how every individual MP voted: voor (for), tegen (against), onthouden (abstained), or afwezig (absent). That’s 531,000 individual vote records.

+

This is an extraordinary dataset. But in raw form it’s just a table of votes. The interesting question is: can we extract structure — left vs. right, progressive vs. conservative, governing vs. opposition — purely from the pattern of who votes with whom?

+

The answer is yes, and the method is surprisingly elegant.

+
+
+
+

Step 1: Turning Votes into Geometry

+

Each motion is a snapshot of political alignment. For each motion, we know which MPs voted together and which voted apart. If every PvdA and GroenLinks MP votes the same way almost every time, that tells us something. If PVV and CDA MPs diverge consistently, that tells us something too.

+

I represent this with Singular Value Decomposition (SVD) on the MP × motion matrix:

    -
  • Family reunification for AMV status holders (Boomsma motion, 2025) — immigration policy at its most contested
    • -
  • Nuclear weapons and NATO (Dobbe motion, 2025) — whether to push for nuclear disarmament within the alliance
    • -
  • Long COVID research funding (Kostic motion, 2025) — healthcare commitments that split parties along unexpected lines
    • -
  • Cormorant population management (Kostic motion, 2025) — agricultural vs. ecological interests in a literal bird-counting exercise
    • +
  • Rows: individual MPs (and party actors for collective votes)
    • +
  • Columns: motions
    • +
  • Values: +1 (voor), -1 (tegen), 0 (absent/abstain)
- -

The narrowest non-tie votes are razor-thin too: Wilders' asylum emergency stop motion lost by the slimmest margin (5 parties for, 16 tied — effectively blocked), while Marijnissen's motion against private equity in GP practices nearly flipped the other way (16 for, 5 tied). On a different day, a different MP showing up, Dutch immigration and healthcare policy could have shifted.

- -

More broadly, over 15,000 motions had winning margins below 55% — these are the genuinely contested decisions, not the rubber stamps. At the other extreme, about 3,700 motions passed with 95%+ support: the uncontroversial consensus items that rarely make headlines.

- -

The Pipeline Architecture

+

SVD finds the dominant axes of variation — the directions along which the chamber disagrees most. The first component almost always corresponds to a left-right axis. The second typically captures something like progressive-traditionalist or libertarian-authoritarian. The key point: the axes emerge from the math, not from any labeling on my part.

+

I request 50 SVD dimensions per window — but the actual dimensionality is constrained by min(n_MPs, n_motions) - 1. Sparse windows (early years, partial quarters) produce fewer meaningful dimensions. The pipeline handles this gracefully, storing whatever k_used is for each window so downstream fusion always works with the actual vector length.

+
+

Making Windows Comparable: Procrustes Alignment

+

Running SVD independently per window creates a subtle problem: SVD axes are arbitrarily oriented. The “left-right” axis from 2020-Q3 and the “left-right” axis from 2021-Q1 might point in completely different directions — even if the underlying politics barely changed. You can’t just stack the coordinates and call it a trajectory.

+

The fix is Procrustes alignment: given two sets of party/MP positions across consecutive windows, find the rotation matrix R that best maps one onto the other (minimizing the Frobenius norm of the difference), using MPs who appear in both windows as anchors:

+
R = argmin_R ||A - B @ R||_F,  subject to R'R = I
+

This is solved cleanly via SVD of the cross-covariance matrix (a nice piece of mathematical symmetry — SVD to build the space, SVD to align it). The result: a continuous track for every party from 2019 to 2026, where position changes reflect genuine political movement rather than axis flips.

+

High Procrustes disparity between consecutive windows — where alignment is poor even with the best rotation — is itself a signal: it suggests a structural political shift, not just individual drift.

+
+
+
+
+

Step 2: What Each Motion Is Actually About

+

Voting patterns tell us who agrees, but not why. For that, I add text embeddings — dense vector representations of each motion’s content using a language model.

+

I use qwen/qwen3-embedding-4b via OpenRouter — a 4-billion parameter multilingual model that produces 2560-dimensional vectors with strong Dutch-language support. For each motion, I embed the richest text available: full parliamentary body text when we have it (94% of the 29,570 motions after an enrichment pass against the Tweede Kamer API), falling back to the summary description or title otherwise.

+

This lets us do something powerful: find motions that are genuinely similar in topic, not just in voting pattern. Two motions about nitrogen policy from 2020 and 2023 might have very different vote splits (different coalitions, different political moment) but near-identical text embeddings. That’s a meaningful connection.

+
+
+
+

Step 3: Fused Embeddings — The Best of Both Worlds

+

SVD gives the political-structural signal: how does this motion split the chamber? Text embeddings give the semantic signal: what is this motion about?

+

I concatenate both into a fused vector per motion per window:

+
fused = [svd_dims (typically 50)] + [text_dims (2560)] = typically 2610 dimensions
+

The actual dimension varies slightly because SVD dimensionality adapts to window density — the code stores svd_dims and text_dims per row so nothing downstream has to assume a fixed size.

+

This fused representation powers the similarity search. Two motions are “close” only if they’re about a similar topic and they produce a similar political split. This filters out spurious matches — two motions might both be controversial (close 50/50 votes) but on completely unrelated things, and the text component separates them.

+
+
+
+

The Numbers: What We’re Working With

+

After the full pipeline run:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
YearMotions
2016162
2017126
2018124
20193,374
20204,223
20214,283
20224,115
20233,272
20243,965
20253,712
20262,214
Total29,570
+

The 2022 spike is striking — over 4,000 motions in a single year. This was the year the Rutte IV coalition took office amid intense debates on energy prices, housing, the war in Ukraine, and the ongoing nitrogen crisis. 2023 is similarly dense at 3,272 motions, culminating in the November election that brought PVV to its historic first-place finish.

+

Early years (2016–2018) use annual windows because the data is too sparse for meaningful quarterly SVD. From 2019 onwards, everything runs quarterly, giving us 41 windows in total.

+

The similarity cache holds 409,938 precomputed pairs — top 10 neighbors per motion per window — making lookup instant at query time.

+
+
+
+

Interesting Findings

+
+

The 2022–2023 Polarization Surge

+

2022 and 2023 together account for more than a quarter of all motions in the dataset. In the SVD positions for 2022, the distance between the governing coalition (VVD, D66, CDA, CU) and the opposition (PVV, SP, FvD) is near its maximum. The nitrogen crisis and energy policy debates forced unusually sharp coalition discipline — which shows up geometrically as well-separated clusters.

+

2023 continued the intensity, and the Procrustes-aligned trajectory shows the party positions in 2023-Q4 and 2024-Q1 shifting noticeably as the new coalition began to form.

+
+
+

BBB’s Geometric Arrival

+

When BBB (BoerBurgerBeweging) entered parliament in 2023 with a historic 16 seats, their SVD position placed them between PVV and CDA — exactly matching their policy profile: agrarian-nationalist populism with Catholic-provincial roots. The model found this without being told. That’s a good sanity check that the geometry is capturing something real.

+
+
+

The Strange Case of “Verworpen.”

+

Motions rejected without debate are recorded with the title “Verworpen.” (Rejected.). There are hundreds of these. Because they share a 9-character title, their text embeddings are identical — cosine similarity 1.0 to every other “Verworpen.” in the cache. Technically correct; semantically meaningless. The UI layer filters these out.

+

It’s a reminder that data quality surprises emerge at scale. I found three or four similar pathologies (motions withdrawn mid-session, duplicate API records) that required explicit handling.

+
+
+

Party Cohesion as a Signal

+

Party cohesion — how often all MPs of a party vote identically — varies enormously. SGP and CU are near-perfect blocs. PvdA/GroenLinks (post-2023 merger) is similarly tight. VVD shows the most internal variation, which tracks with what you’d expect from a governing party managing coalition discipline across conflicting wings.

+

In earlier years (2019–2020), before the GroenLinks-PvdA merger, GroenLinks occasionally splits on security and defense policy — visible in the SVD as individual MP positions diverging from the party centroid.

+
+
+
+
+

The Pipeline Architecture

Single DuckDB database, modular Python pipeline, no cloud infrastructure:

-
API (Tweede Kamer OData)
-  → download_past_year.py        → motions table (28,304 rows)
-
+
API (Tweede Kamer OData) 
+  → download_past_year.py 
+  → motions table (29,570 rows)
+  
 motions
-  → extract_mp_votes.py          → mp_votes table (508,765 rows)
-  → sync_motion_content.py       → body_text enrichment (~94% coverage)
-  → svd_pipeline.py              → svd_vectors table (73,165 rows, 41 windows)
-
-svd_vectors
-  → similarity/compute.py        → similarity_cache (top-10 per window)

-
The similarity computation is pure NumPy: load all SVD vectors for a window, pad to uniform length, L2-normalize, compute the full cosine similarity matrix via a single matrix multiply, then extract top-k neighbors. For a 4,000-motion quarter, that's a 4000×4000 matrix operation — fast enough that batching isn't needed.

-
The database sits at ~18 GB on disk — the full parliamentary text for 26,000+ motions accounts for most of that.

-
-
What the Axes Actually Mean

-
-
One of the trickiest problems was labeling the SVD axes. The first component reliably captures left-right economics. But components 3 through 10? The mathematical procedure is sound — SVD finds the directions of maximum variance — but the meaning of each axis has to be derived from the actual motions that load heavily on it.

-
-
I solved this by extracting the top 50 motions per component (by absolute loading score), then analyzing their content. Some clear patterns emerged:

-
-
    
-
  • Component 1: Fiscal-economic policy vs. social welfare and international rights — the classic left-right split.
    • 
-
  • Component 2: Nationalist vs. multilateralist orientation — PVV/FvD on one side, Volt/GroenLinks-PvdA on the other.
    • 
-
  • Component 3: Welfare state vs. defense spending — flip of the usual axis (with SP/PvdD on the pro-welfare side, VVD/SGP on the pro-defense side).
    • 
-

-
-

-How much do the first two axes actually capture? In a single-window SVD (current parliament), PC1 explains ~29% of the variance and PC2 explains ~11.5% — together accounting for ~41% of all voting variation. PC3 adds another 8.6%, but from there it drops off sharply: PC4 is under 9%, and components 5–8 each contribute 3–6%. The classic "scree plot" elbow is clear: the first two dimensions carry the signal, the rest is real but diminishing. When looking across all time windows with Procrustes alignment, the picture flattens considerably — PC1 and PC2 each explain ~14.6% and ~13.1% respectively — because aligning 41 different windows distributes variance more evenly. The multi-window perspective is more conservative, but the message is the same: Dutch politics is largely two-dimensional.
-
-Scree plot — multi-window Procrustes-aligned SVD
-Scree plot across 41 aligned quarterly windows. PC1 = 14.6%, PC2 = 13.1%.
-

-
-
What's Next

-
-
Motion explorer: Given a motion, retrieve the 10 most politically similar ones from across the decade. Trace how a policy debate evolved — who championed it, how the coalitions shifted.

-
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Party trajectory animation: Procrustes-aligned positions, animated year by year. Watch GroenLinks-PvdA's pre-merger convergence, watch PVV consolidate its flank, watch new parties arrive and find their geometric home.

-
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Cross-party coalition patterns: Which topics produce unusual coalition configurations — motions where the normal left-right split breaks down and unexpected alliances form.

-
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Cabinet crisis detection: Track coalition cohesion over time. When do coalition parties start voting against each other? The Procrustes disparity between consecutive windows is itself a signal of structural political shifts.

-
-
Reproducibility

-
# Download historical data
-python scripts/download_past_year.py --start-date 2016-01-01 --end-date 2026-01-01
-
-# Run full pipeline (SVD, similarity cache)
-python -m pipeline.run_pipeline --db-path data/motions.db \
-    --start-date 2016-01-01 --end-date 2026-01-01 \
-    --window-size quarterly --text-batch-size 200
-
-# Enrich with full motion body text
-python scripts/sync_motion_content.py --db-path data/motions.db

-
All computation — SVD, similarity — runs locally on a single machine. No cloud services, no GPU required.

-
+  → extract_mp_votes.py      → mp_votes table (531,869 rows)
+  → sync_motion_content.py   → body_text enrichment (~94%)
+  → text_pipeline.py         → embeddings table (28,680 rows, qwen3-embedding-4b via OpenRouter)
+  → svd_pipeline.py          → svd_vectors table (73,172 rows, 41 windows)
+  
+svd_vectors + embeddings
+  → fusion.py                → fused_embeddings table (41,422 rows)
+  
+fused_embeddings
+  → similarity/compute.py    → similarity_cache table (409,938 rows, top-10 per window)
+

The similarity computation is pure NumPy: load all fused vectors for a window, pad to uniform length, L2-normalize, compute the full N×N cosine similarity matrix via a single matrix multiply (normalized @ normalized.T), then extract top-k neighbors per row with np.argpartition. For a 4,000-motion quarter, that’s a 4000×4000 matrix operation — fast enough that it’s not worth batching.

+

The database sits at 18 GB on disk — up from ~3 GB before body text enrichment. The full parliamentary text for 28,000+ motions accounts for most of that growth.

+
+
+
+

What I Built On Top

+

The pipeline above is the foundation. Here’s what it now powers:

+

Overton Window analysis: Using the SVD compass and vote records, I tested whether the Dutch Overton window shifted after PVV’s November 2023 election victory. The answer: it widened, but through right-wing moderation rather than centrist conversion. Centrist support for right-wing motions rose from 25% to 51%, while centrists actually moved left on the SVD compass. The full analysis covers 3,030 classified right-wing motions, 2D extremity scoring, quarterly trajectories, and mechanism classification. Read the full report →

+

2D extremity scoring: Every motion in the database has been scored by an LLM on two independent dimensions: stylistic extremity (rhetorical hostility) and material impact (policy consequence). They’re only moderately correlated (r = 0.43), which matters: right-wing motions post-2024 became milder on both dimensions, not just in tone.

+

Streamlit Explorer: An interactive dashboard where you can browse the SVD compass, trace party trajectories over time, explore centrist support trends, and browse individual motions with their extremity scores and similarity matches. The same data and methods that drive the analysis reports power the live exploration interface.

+
+
+
+

Reproducibility

+
# Download historical data
+python scripts/download_past_year.py --start-date 2016-01-01 --end-date 2026-01-01
+
+# Run full pipeline (SVD, text embeddings, fusion, similarity cache)
+python -m pipeline.run_pipeline --db-path data/motions.db \
+    --start-date 2016-01-01 --end-date 2026-01-01 \
+    --window-size quarterly --text-batch-size 200
+
+# Enrich with full motion body text
+python scripts/sync_motion_content.py --db-path data/motions.db
+

The DB grows to ~18 GB for the full dataset including body text. All computation — SVD, fusion, similarity — runs locally on a single machine.

Democracy is more legible than it looks.

- - +
+ +
+ + +
+ + + + + \ No newline at end of file diff --git a/thoughts/blog-post-political-compass.md b/thoughts/blog-post-political-compass.md index 7f904bf..9dcb1ba 100644 --- a/thoughts/blog-post-political-compass.md +++ b/thoughts/blog-post-political-compass.md @@ -1,4 +1,4 @@ -# Mapping Dutch Democracy: Building a Political Compass from 28,000+ Parliamentary Votes +# Mapping Dutch Democracy: Building a Political Compass from 29,000+ Parliamentary Votes *What if you could take every motion voted on in the Dutch Parliament over the past decade and automatically plot parties and MPs on a political map — with zero manual labeling?* @@ -8,7 +8,7 @@ That's exactly what this project does. Here's how I built it, what I had to solv ## The Starting Point: Open Data, Hidden Structure -The Dutch Parliament publishes every vote — every *motie*, every *amendement*, every *besluit* — in an open OData API. We're talking over **28,000 motions** spanning 2016 to 2026, with a record of how every individual MP voted: *voor* (for), *tegen* (against), *onthouden* (abstained), or *afwezig* (absent). That's 506,000 individual vote records. +The Dutch Parliament publishes every vote — every *motie*, every *amendement*, every *besluit* — in an open OData API. We're talking over **29,500 motions** spanning 2016 to 2026, with a record of how every individual MP voted: *voor* (for), *tegen* (against), *onthouden* (abstained), or *afwezig* (absent). That's 531,000 individual vote records. This is an extraordinary dataset. But in raw form it's just a table of votes. The interesting question is: can we extract *structure* — left vs. right, progressive vs. conservative, governing vs. opposition — purely from the pattern of who votes with whom? @@ -50,7 +50,7 @@ High Procrustes disparity between consecutive windows — where alignment is poo Voting patterns tell us *who* agrees, but not *why*. For that, I add **text embeddings** — dense vector representations of each motion's content using a language model. -I use **`qwen/qwen3-embedding-4b`** via OpenRouter — a 4-billion parameter multilingual model that produces 2560-dimensional vectors with strong Dutch-language support. For each motion, I embed the richest text available: full parliamentary body text when we have it (94% of the 28,172 motions after an enrichment pass against the Tweede Kamer API), falling back to the summary description or title otherwise. Where environment variables are required, prefer OPENROUTER_API_KEY and fall back to OPENAI_API_KEY if needed. +I use **`qwen/qwen3-embedding-4b`** via OpenRouter — a 4-billion parameter multilingual model that produces 2560-dimensional vectors with strong Dutch-language support. For each motion, I embed the richest text available: full parliamentary body text when we have it (94% of the 29,570 motions after an enrichment pass against the Tweede Kamer API), falling back to the summary description or title otherwise. This lets us do something powerful: find motions that are genuinely similar in *topic*, not just in voting pattern. Two motions about nitrogen policy from 2020 and 2023 might have very different vote splits (different coalitions, different political moment) but near-identical text embeddings. That's a meaningful connection. @@ -78,24 +78,24 @@ After the full pipeline run: | Year | Motions | |------|---------| -| 2016 | 132 | -| 2017 | 30 | -| 2018 | 100 | +| 2016 | 162 | +| 2017 | 126 | +| 2018 | 124 | | 2019 | 3,374 | -| 2020 | 4,228 | -| 2021 | 4,289 | -| 2022 | 4,116 | +| 2020 | 4,223 | +| 2021 | 4,283 | +| 2022 | 4,115 | | 2023 | 3,272 | -| 2024 | 3,968 | -| 2025 | 3,715 | -| 2026 | 948 | -| **Total** | **28,172** | +| 2024 | 3,965 | +| 2025 | 3,712 | +| 2026 | 2,214 | +| **Total** | **29,570** | The 2022 spike is striking — over 4,000 motions in a single year. This was the year the Rutte IV coalition took office amid intense debates on energy prices, housing, the war in Ukraine, and the ongoing nitrogen crisis. 2023 is similarly dense at 3,272 motions, culminating in the November election that brought PVV to its historic first-place finish. -Early years (2016–2018) use annual windows because the data is too sparse for meaningful quarterly SVD. From 2019 onwards, everything runs quarterly, giving us 38 windows in total. +Early years (2016–2018) use annual windows because the data is too sparse for meaningful quarterly SVD. From 2019 onwards, everything runs quarterly, giving us 41 windows in total. -The similarity cache holds **405,216 precomputed pairs** — top 10 neighbors per motion per window — making lookup instant at query time. +The similarity cache holds **409,938 precomputed pairs** — top 10 neighbors per motion per window — making lookup instant at query time. --- @@ -132,36 +132,36 @@ Single DuckDB database, modular Python pipeline, no cloud infrastructure: ``` API (Tweede Kamer OData) → download_past_year.py - → motions table (28,172 rows) + → motions table (29,570 rows) motions - → extract_mp_votes.py → mp_votes table (506,336 rows) - → sync_motion_content.py → body_text enrichment (26,447 motions, ~94%) - → text_pipeline.py → embeddings table (28,172 rows, qwen3-embedding-4b via OpenRouter). Configuration: prefer OPENROUTER_API_KEY with OPENAI_API_KEY as a fallback. - → svd_pipeline.py → svd_vectors table (54,150 rows, 38 windows) + → extract_mp_votes.py → mp_votes table (531,869 rows) + → sync_motion_content.py → body_text enrichment (~94%) + → text_pipeline.py → embeddings table (28,680 rows, qwen3-embedding-4b via OpenRouter) + → svd_pipeline.py → svd_vectors table (73,172 rows, 41 windows) svd_vectors + embeddings - → fusion.py → fused_embeddings table (40,522 rows) + → fusion.py → fused_embeddings table (41,422 rows) fused_embeddings - → similarity/compute.py → similarity_cache table (405,216 rows, top-10 per window) + → similarity/compute.py → similarity_cache table (409,938 rows, top-10 per window) ``` The similarity computation is pure NumPy: load all fused vectors for a window, pad to uniform length, L2-normalize, compute the full `N×N` cosine similarity matrix via a single matrix multiply (`normalized @ normalized.T`), then extract top-k neighbors per row with `np.argpartition`. For a 4,000-motion quarter, that's a 4000×4000 matrix operation — fast enough that it's not worth batching. -The database sits at 15 GB on disk — up from ~3 GB before body text enrichment. The full parliamentary text for 26,000+ motions accounts for most of that growth. +The database sits at 18 GB on disk — up from ~3 GB before body text enrichment. The full parliamentary text for 28,000+ motions accounts for most of that growth. --- -## What's Next +## What I Built On Top -**Motion explorer**: Given a motion, retrieve the 10 most politically and semantically similar ones from across the decade. Trace how a policy debate evolved — who championed it, how the coalitions shifted. +The pipeline above is the foundation. Here's what it now powers: -**Party trajectory animation**: Procrustes-aligned positions, animated year by year. Watch D66 drift post-2021, watch PVV consolidate its flank, watch new parties arrive and find their geometric home. +**Overton Window analysis**: Using the SVD compass and vote records, I tested whether the Dutch Overton window shifted after PVV's November 2023 election victory. The answer: it widened, but through right-wing moderation rather than centrist conversion. Centrist support for right-wing motions rose from 25% to 51%, while centrists actually moved *left* on the SVD compass. The full analysis covers 3,030 classified right-wing motions, 2D extremity scoring, quarterly trajectories, and mechanism classification. [Read the full report →](overton_report.html) -**Cross-party coalition patterns**: The fused embeddings let us ask which topics produce unusual coalition configurations — motions where the normal left-right split breaks down and unexpected alliances form. +**2D extremity scoring**: Every motion in the database has been scored by an LLM on two independent dimensions: stylistic extremity (rhetorical hostility) and material impact (policy consequence). They're only moderately correlated (r = 0.43), which matters: right-wing motions post-2024 became milder on *both* dimensions, not just in tone. -**The controversy index**: `1 - winning_margin` gives a controversy score per motion. The most contested votes — close margins, high-salience topics — tell a different story than the headline political narratives. +**Streamlit Explorer**: An interactive dashboard where you can browse the SVD compass, trace party trajectories over time, explore centrist support trends, and browse individual motions with their extremity scores and similarity matches. The same data and methods that drive the analysis reports power the live exploration interface. --- @@ -180,6 +180,6 @@ python -m pipeline.run_pipeline --db-path data/motions.db \ python scripts/sync_motion_content.py --db-path data/motions.db ``` -The DB grows to ~15 GB for the full dataset including body text. All computation — SVD, fusion, similarity — runs locally on a single machine. +The DB grows to ~18 GB for the full dataset including body text. All computation — SVD, fusion, similarity — runs locally on a single machine. Democracy is more legible than it looks. diff --git a/thoughts/blog-post-political-compass.qmd b/thoughts/blog-post-political-compass.qmd new file mode 100644 index 0000000..89afb1f --- /dev/null +++ b/thoughts/blog-post-political-compass.qmd @@ -0,0 +1,190 @@ +--- +title: "Mapping Dutch Democracy: Building a Political Compass from 29,000+ Parliamentary Votes" +author: "Stemwijzer Analysis" +date: today +format: html +--- + +*What if you could take every motion voted on in the Dutch Parliament over the past decade and automatically plot parties and MPs on a political map — with zero manual labeling?* + +That's exactly what this project does. Here's how I built it, what I had to solve along the way, and what it revealed about Dutch political dynamics. + +--- + +## The Starting Point: Open Data, Hidden Structure + +The Dutch Parliament publishes every vote — every *motie*, every *amendement*, every *besluit* — in an open OData API. We're talking over **29,500 motions** spanning 2016 to 2026, with a record of how every individual MP voted: *voor* (for), *tegen* (against), *onthouden* (abstained), or *afwezig* (absent). That's 531,000 individual vote records. + +This is an extraordinary dataset. But in raw form it's just a table of votes. The interesting question is: can we extract *structure* — left vs. right, progressive vs. conservative, governing vs. opposition — purely from the pattern of who votes with whom? + +The answer is yes, and the method is surprisingly elegant. + +--- + +## Step 1: Turning Votes into Geometry + +Each motion is a snapshot of political alignment. For each motion, we know which MPs voted together and which voted apart. If every PvdA and GroenLinks MP votes the same way almost every time, that tells us something. If PVV and CDA MPs diverge consistently, that tells us something too. + +I represent this with **Singular Value Decomposition (SVD)** on the MP × motion matrix: + +- Rows: individual MPs (and party actors for collective votes) +- Columns: motions +- Values: +1 (voor), -1 (tegen), 0 (absent/abstain) + +SVD finds the dominant axes of variation — the directions along which the chamber disagrees most. The first component almost always corresponds to a left-right axis. The second typically captures something like progressive-traditionalist or libertarian-authoritarian. The key point: **the axes emerge from the math, not from any labeling on my part.** + +I request 50 SVD dimensions per window — but the actual dimensionality is constrained by `min(n_MPs, n_motions) - 1`. Sparse windows (early years, partial quarters) produce fewer meaningful dimensions. The pipeline handles this gracefully, storing whatever `k_used` is for each window so downstream fusion always works with the actual vector length. + +### Making Windows Comparable: Procrustes Alignment + +Running SVD independently per window creates a subtle problem: SVD axes are **arbitrarily oriented**. The "left-right" axis from 2020-Q3 and the "left-right" axis from 2021-Q1 might point in completely different directions — even if the underlying politics barely changed. You can't just stack the coordinates and call it a trajectory. + +The fix is **Procrustes alignment**: given two sets of party/MP positions across consecutive windows, find the rotation matrix R that best maps one onto the other (minimizing the Frobenius norm of the difference), using MPs who appear in both windows as anchors: + +``` +R = argmin_R ||A - B @ R||_F, subject to R'R = I +``` + +This is solved cleanly via SVD of the cross-covariance matrix (a nice piece of mathematical symmetry — SVD to build the space, SVD to align it). The result: a continuous track for every party from 2019 to 2026, where position changes reflect genuine political movement rather than axis flips. + +High Procrustes disparity between consecutive windows — where alignment is poor even with the best rotation — is itself a signal: it suggests a structural political shift, not just individual drift. + +--- + +## Step 2: What Each Motion Is Actually About + +Voting patterns tell us *who* agrees, but not *why*. For that, I add **text embeddings** — dense vector representations of each motion's content using a language model. + +I use **`qwen/qwen3-embedding-4b`** via OpenRouter — a 4-billion parameter multilingual model that produces 2560-dimensional vectors with strong Dutch-language support. For each motion, I embed the richest text available: full parliamentary body text when we have it (94% of the 29,570 motions after an enrichment pass against the Tweede Kamer API), falling back to the summary description or title otherwise. + +This lets us do something powerful: find motions that are genuinely similar in *topic*, not just in voting pattern. Two motions about nitrogen policy from 2020 and 2023 might have very different vote splits (different coalitions, different political moment) but near-identical text embeddings. That's a meaningful connection. + +--- + +## Step 3: Fused Embeddings — The Best of Both Worlds + +SVD gives the political-structural signal: *how does this motion split the chamber?* Text embeddings give the semantic signal: *what is this motion about?* + +I concatenate both into a **fused vector** per motion per window: + +``` +fused = [svd_dims (typically 50)] + [text_dims (2560)] = typically 2610 dimensions +``` + +The actual dimension varies slightly because SVD dimensionality adapts to window density — the code stores `svd_dims` and `text_dims` per row so nothing downstream has to assume a fixed size. + +This fused representation powers the similarity search. Two motions are "close" only if they're about a similar *topic* **and** they produce a similar *political split*. This filters out spurious matches — two motions might both be controversial (close 50/50 votes) but on completely unrelated things, and the text component separates them. + +--- + +## The Numbers: What We're Working With + +After the full pipeline run: + +| Year | Motions | +|------|---------| +| 2016 | 162 | +| 2017 | 126 | +| 2018 | 124 | +| 2019 | 3,374 | +| 2020 | 4,223 | +| 2021 | 4,283 | +| 2022 | 4,115 | +| 2023 | 3,272 | +| 2024 | 3,965 | +| 2025 | 3,712 | +| 2026 | 2,214 | +| **Total** | **29,570** | + +The 2022 spike is striking — over 4,000 motions in a single year. This was the year the Rutte IV coalition took office amid intense debates on energy prices, housing, the war in Ukraine, and the ongoing nitrogen crisis. 2023 is similarly dense at 3,272 motions, culminating in the November election that brought PVV to its historic first-place finish. + +Early years (2016–2018) use annual windows because the data is too sparse for meaningful quarterly SVD. From 2019 onwards, everything runs quarterly, giving us 41 windows in total. + +The similarity cache holds **409,938 precomputed pairs** — top 10 neighbors per motion per window — making lookup instant at query time. + +--- + +## Interesting Findings + +### The 2022–2023 Polarization Surge + +2022 and 2023 together account for more than a quarter of all motions in the dataset. In the SVD positions for 2022, the distance between the governing coalition (VVD, D66, CDA, CU) and the opposition (PVV, SP, FvD) is near its maximum. The nitrogen crisis and energy policy debates forced unusually sharp coalition discipline — which shows up geometrically as well-separated clusters. + +2023 continued the intensity, and the Procrustes-aligned trajectory shows the party positions in 2023-Q4 and 2024-Q1 shifting noticeably as the new coalition began to form. + +### BBB's Geometric Arrival + +When BBB (BoerBurgerBeweging) entered parliament in 2023 with a historic 16 seats, their SVD position placed them between PVV and CDA — exactly matching their policy profile: agrarian-nationalist populism with Catholic-provincial roots. The model found this without being told. That's a good sanity check that the geometry is capturing something real. + +### The Strange Case of "Verworpen." + +Motions rejected without debate are recorded with the title "Verworpen." (Rejected.). There are hundreds of these. Because they share a 9-character title, their text embeddings are **identical** — cosine similarity 1.0 to every other "Verworpen." in the cache. Technically correct; semantically meaningless. The UI layer filters these out. + +It's a reminder that **data quality surprises emerge at scale**. I found three or four similar pathologies (motions withdrawn mid-session, duplicate API records) that required explicit handling. + +### Party Cohesion as a Signal + +Party cohesion — how often all MPs of a party vote identically — varies enormously. SGP and CU are near-perfect blocs. PvdA/GroenLinks (post-2023 merger) is similarly tight. VVD shows the most internal variation, which tracks with what you'd expect from a governing party managing coalition discipline across conflicting wings. + +In earlier years (2019–2020), before the GroenLinks-PvdA merger, GroenLinks occasionally splits on security and defense policy — visible in the SVD as individual MP positions diverging from the party centroid. + +--- + +## The Pipeline Architecture + +Single DuckDB database, modular Python pipeline, no cloud infrastructure: + +``` +API (Tweede Kamer OData) + → download_past_year.py + → motions table (29,570 rows) + +motions + → extract_mp_votes.py → mp_votes table (531,869 rows) + → sync_motion_content.py → body_text enrichment (~94%) + → text_pipeline.py → embeddings table (28,680 rows, qwen3-embedding-4b via OpenRouter) + → svd_pipeline.py → svd_vectors table (73,172 rows, 41 windows) + +svd_vectors + embeddings + → fusion.py → fused_embeddings table (41,422 rows) + +fused_embeddings + → similarity/compute.py → similarity_cache table (409,938 rows, top-10 per window) +``` + +The similarity computation is pure NumPy: load all fused vectors for a window, pad to uniform length, L2-normalize, compute the full `N×N` cosine similarity matrix via a single matrix multiply (`normalized @ normalized.T`), then extract top-k neighbors per row with `np.argpartition`. For a 4,000-motion quarter, that's a 4000×4000 matrix operation — fast enough that it's not worth batching. + +The database sits at 18 GB on disk — up from ~3 GB before body text enrichment. The full parliamentary text for 28,000+ motions accounts for most of that growth. + +--- + +## What I Built On Top + +The pipeline above is the foundation. Here's what it now powers: + +**Overton Window analysis**: Using the SVD compass and vote records, I tested whether the Dutch Overton window shifted after PVV's November 2023 election victory. The answer: it widened, but through right-wing moderation rather than centrist conversion. Centrist support for right-wing motions rose from 25% to 51%, while centrists actually moved *left* on the SVD compass. The full analysis covers 3,030 classified right-wing motions, 2D extremity scoring, quarterly trajectories, and mechanism classification. [Read the full report →](../reports/overton_window/overton_report.html) + +**2D extremity scoring**: Every motion in the database has been scored by an LLM on two independent dimensions: stylistic extremity (rhetorical hostility) and material impact (policy consequence). They're only moderately correlated (r = 0.43), which matters: right-wing motions post-2024 became milder on *both* dimensions, not just in tone. + +**Streamlit Explorer**: An interactive dashboard where you can browse the SVD compass, trace party trajectories over time, explore centrist support trends, and browse individual motions with their extremity scores and similarity matches. The same data and methods that drive the analysis reports power the live exploration interface. + +--- + +## Reproducibility + +```bash +# Download historical data +python scripts/download_past_year.py --start-date 2016-01-01 --end-date 2026-01-01 + +# Run full pipeline (SVD, text embeddings, fusion, similarity cache) +python -m pipeline.run_pipeline --db-path data/motions.db \ + --start-date 2016-01-01 --end-date 2026-01-01 \ + --window-size quarterly --text-batch-size 200 + +# Enrich with full motion body text +python scripts/sync_motion_content.py --db-path data/motions.db +``` + +The DB grows to ~18 GB for the full dataset including body text. All computation — SVD, fusion, similarity — runs locally on a single machine. + +Democracy is more legible than it looks. diff --git a/thoughts/blog-post-political-compass_files/libs/bootstrap/bootstrap-ead859a0cde6e94fc21d93203ba7f4bc.min.css b/thoughts/blog-post-political-compass_files/libs/bootstrap/bootstrap-ead859a0cde6e94fc21d93203ba7f4bc.min.css new file mode 100644 index 0000000..76fd0fe --- /dev/null +++ b/thoughts/blog-post-political-compass_files/libs/bootstrap/bootstrap-ead859a0cde6e94fc21d93203ba7f4bc.min.css @@ -0,0 +1,12 @@ +/*! + * Bootstrap v5.3.1 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */:root,[data-bs-theme=light]{--bs-blue: #0d6efd;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #d63384;--bs-red: #dc3545;--bs-orange: #fd7e14;--bs-yellow: #ffc107;--bs-green: #198754;--bs-teal: #20c997;--bs-cyan: #0dcaf0;--bs-black: #000;--bs-white: #ffffff;--bs-gray: 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1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: rgb(253.26, 253.63, 253.98);--bs-navbar-brand-hover-color: rgb(252.58, 253.55, 254.98);--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25;--bs-navbar-toggler-padding-x: 0;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgb%28253.26, 253.63, 253.98%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(253.26, 253.63, 253.98, 0);--bs-navbar-toggler-border-radius: 0.375rem;--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgb%28253.26, 253.63, 253.98%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgb%28253.26, 253.63, 253.98%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.375rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.375rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: 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0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #212529;--bs-accordion-bg: #ffffff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s 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reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='rgb%28109.8, 168, 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: rgb(221.7, 222.3, 222.9);--bs-pagination-border-radius: 0.375rem;--bs-pagination-hover-color: rgb(10.4, 88, 202.4);--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: rgb(221.7, 222.3, 222.9);--bs-pagination-focus-color: rgb(10.4, 88, 202.4);--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: rgb(221.7, 222.3, 222.9);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.25rem}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.375rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.375rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.375rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: rgb(221.7, 222.3, 222.9);--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.375rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: 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";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.375rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #ffffff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: rgb(221.7, 222.3, 222.9);--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: rgb(221.7, 222.3, 222.9);--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid 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.modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down 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0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue","Noto Sans","Liberation Sans",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] 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56.6);--quarto-scss-export-yellow-500: #ffc107;--quarto-scss-export-yellow-600: rgb(204, 154.4, 5.6);--quarto-scss-export-yellow-700: rgb(153, 115.8, 4.2);--quarto-scss-export-yellow-800: rgb(102, 77.2, 2.8);--quarto-scss-export-yellow-900: rgb(51, 38.6, 1.4);--quarto-scss-export-green-100: rgb(209, 231, 220.8);--quarto-scss-export-green-200: rgb(163, 207, 186.6);--quarto-scss-export-green-300: rgb(117, 183, 152.4);--quarto-scss-export-green-400: rgb(71, 159, 118.2);--quarto-scss-export-green-500: #198754;--quarto-scss-export-green-600: rgb(20, 108, 67.2);--quarto-scss-export-green-700: rgb(15, 81, 50.4);--quarto-scss-export-green-800: rgb(10, 54, 33.6);--quarto-scss-export-green-900: rgb(5, 27, 16.8);--quarto-scss-export-teal-100: rgb(210.4, 244.2, 234.2);--quarto-scss-export-teal-200: rgb(165.8, 233.4, 213.4);--quarto-scss-export-teal-300: rgb(121.2, 222.6, 192.6);--quarto-scss-export-teal-400: rgb(76.6, 211.8, 171.8);--quarto-scss-export-teal-500: #20c997;--quarto-scss-export-teal-600: rgb(25.6, 160.8, 120.8);--quarto-scss-export-teal-700: rgb(19.2, 120.6, 90.6);--quarto-scss-export-teal-800: rgb(12.8, 80.4, 60.4);--quarto-scss-export-teal-900: rgb(6.4, 40.2, 30.2);--quarto-scss-export-cyan-100: rgb(206.6, 244.4, 252);--quarto-scss-export-cyan-200: rgb(158.2, 233.8, 249);--quarto-scss-export-cyan-300: rgb(109.8, 223.2, 246);--quarto-scss-export-cyan-400: rgb(61.4, 212.6, 243);--quarto-scss-export-cyan-500: #0dcaf0;--quarto-scss-export-cyan-600: rgb(10.4, 161.6, 192);--quarto-scss-export-cyan-700: rgb(7.8, 121.2, 144);--quarto-scss-export-cyan-800: rgb(5.2, 80.8, 96);--quarto-scss-export-cyan-900: rgb(2.6, 40.4, 48);--quarto-scss-export-default: #dee2e6;--quarto-scss-export-secondary: #6c757d;--quarto-scss-export-success: #198754;--quarto-scss-export-info: #0dcaf0;--quarto-scss-export-warning: #ffc107;--quarto-scss-export-danger: #dc3545;--quarto-scss-export-light: #f8f9fa;--quarto-scss-export-dark: #212529;--quarto-scss-export-primary-text-emphasis: rgb(5.2, 44, 101.2);--quarto-scss-export-secondary-text-emphasis: rgb(43.2, 46.8, 50);--quarto-scss-export-success-text-emphasis: rgb(10, 54, 33.6);--quarto-scss-export-info-text-emphasis: rgb(5.2, 80.8, 96);--quarto-scss-export-warning-text-emphasis: rgb(102, 77.2, 2.8);--quarto-scss-export-danger-text-emphasis: rgb(88, 21.2, 27.6);--quarto-scss-export-light-text-emphasis: #495057;--quarto-scss-export-dark-text-emphasis: #495057;--quarto-scss-export-primary-bg-subtle: rgb(206.6, 226, 254.6);--quarto-scss-export-secondary-bg-subtle: rgb(225.6, 227.4, 229);--quarto-scss-export-success-bg-subtle: rgb(209, 231, 220.8);--quarto-scss-export-info-bg-subtle: rgb(206.6, 244.4, 252);--quarto-scss-export-warning-bg-subtle: rgb(255, 242.6, 205.4);--quarto-scss-export-danger-bg-subtle: rgb(248, 214.6, 217.8);--quarto-scss-export-light-bg-subtle: rgb(251.5, 252, 252.5);--quarto-scss-export-dark-bg-subtle: #ced4da;--quarto-scss-export-primary-border-subtle: rgb(158.2, 197, 254.2);--quarto-scss-export-secondary-border-subtle: rgb(196.2, 199.8, 203);--quarto-scss-export-success-border-subtle: rgb(163, 207, 186.6);--quarto-scss-export-info-border-subtle: rgb(158.2, 233.8, 249);--quarto-scss-export-warning-border-subtle: rgb(255, 230.2, 155.8);--quarto-scss-export-danger-border-subtle: rgb(241, 174.2, 180.6);--quarto-scss-export-light-border-subtle: #e9ecef;--quarto-scss-export-dark-border-subtle: #adb5bd;--quarto-scss-export-body-text-align: ;--quarto-scss-export-body-color: #212529;--quarto-scss-export-body-bg: #ffffff;--quarto-scss-export-body-secondary-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-body-secondary-bg: #e9ecef;--quarto-scss-export-body-tertiary-color: rgba(33, 37, 41, 0.5);--quarto-scss-export-body-tertiary-bg: #f8f9fa;--quarto-scss-export-body-emphasis-color: #000;--quarto-scss-export-link-hover-color: rgb(10.4, 88, 202.4);--quarto-scss-export-link-hover-decoration: ;--quarto-scss-export-border-color-translucent: rgba(0, 0, 0, 0.175);--quarto-scss-export-component-active-bg: #0d6efd;--quarto-scss-export-component-active-color: #ffffff;--quarto-scss-export-focus-ring-color: rgba(13, 110, 253, 0.25);--quarto-scss-export-headings-font-family: ;--quarto-scss-export-headings-font-style: ;--quarto-scss-export-display-font-family: ;--quarto-scss-export-display-font-style: ;--quarto-scss-export-text-muted: rgba(33, 37, 41, 0.75);--quarto-scss-export-blockquote-footer-color: #6c757d;--quarto-scss-export-blockquote-border-color: #e9ecef;--quarto-scss-export-hr-bg-color: ;--quarto-scss-export-hr-height: ;--quarto-scss-export-hr-border-color: ;--quarto-scss-export-legend-font-weight: ;--quarto-scss-export-mark-bg: rgb(255, 242.6, 205.4);--quarto-scss-export-table-color: #212529;--quarto-scss-export-table-bg: #ffffff;--quarto-scss-export-table-accent-bg: transparent;--quarto-scss-export-table-th-font-weight: ;--quarto-scss-export-table-striped-color: #212529;--quarto-scss-export-table-striped-bg: rgba(0, 0, 0, 0.05);--quarto-scss-export-table-active-color: #212529;--quarto-scss-export-table-active-bg: rgba(0, 0, 0, 0.1);--quarto-scss-export-table-hover-color: #212529;--quarto-scss-export-table-hover-bg: rgba(0, 0, 0, 0.075);--quarto-scss-export-table-caption-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-input-btn-font-family: ;--quarto-scss-export-input-btn-focus-color: rgba(13, 110, 253, 0.25);--quarto-scss-export-btn-color: #212529;--quarto-scss-export-btn-font-family: ;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #0d6efd;--quarto-scss-export-btn-link-hover-color: rgb(10.4, 88, 202.4);--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #212529;--quarto-scss-export-input-focus-bg: #ffffff;--quarto-scss-export-input-focus-border-color: rgb(134, 182.5, 254);--quarto-scss-export-input-focus-color: #212529;--quarto-scss-export-input-placeholder-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-input-plaintext-color: #212529;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #ffffff;--quarto-scss-export-form-check-input-focus-border: rgb(134, 182.5, 254);--quarto-scss-export-form-check-input-checked-color: #ffffff;--quarto-scss-export-form-check-input-checked-bg-color: #0d6efd;--quarto-scss-export-form-check-input-checked-border-color: #0d6efd;--quarto-scss-export-form-check-input-indeterminate-color: #ffffff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #0d6efd;--quarto-scss-export-form-check-input-indeterminate-border-color: #0d6efd;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: rgb(134, 182.5, 254);--quarto-scss-export-form-switch-checked-color: #ffffff;--quarto-scss-export-input-group-addon-color: #212529;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #212529;--quarto-scss-export-form-select-bg: #ffffff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-form-select-focus-border-color: rgb(134, 182.5, 254);--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #0d6efd;--quarto-scss-export-form-range-thumb-active-bg: rgb(182.4, 211.5, 254.4);--quarto-scss-export-form-range-thumb-disabled-bg: rgba(33, 37, 41, 0.75);--quarto-scss-export-form-file-button-color: #212529;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #198754;--quarto-scss-export-form-feedback-invalid-color: #dc3545;--quarto-scss-export-form-feedback-icon-valid-color: #198754;--quarto-scss-export-form-feedback-icon-invalid-color: #dc3545;--quarto-scss-export-form-valid-color: #198754;--quarto-scss-export-form-valid-border-color: #198754;--quarto-scss-export-form-invalid-color: #dc3545;--quarto-scss-export-form-invalid-border-color: #dc3545;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #0d6efd;--quarto-scss-export-nav-link-hover-color: rgb(10.4, 88, 202.4);--quarto-scss-export-nav-link-disabled-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-nav-tabs-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef rgb(221.7, 222.3, 222.9);--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #ffffff;--quarto-scss-export-nav-pills-link-active-bg: #0d6efd;--quarto-scss-export-nav-pills-link-active-color: #ffffff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #ffffff;--quarto-scss-export-navbar-dark-contrast: #ffffff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #212529;--quarto-scss-export-dropdown-bg: #ffffff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #212529;--quarto-scss-export-dropdown-link-hover-color: #212529;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #0d6efd;--quarto-scss-export-dropdown-link-active-color: #ffffff;--quarto-scss-export-dropdown-link-disabled-color: rgba(33, 37, 41, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #ffffff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #ffffff;--quarto-scss-export-dropdown-dark-link-active-bg: #0d6efd;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #0d6efd;--quarto-scss-export-pagination-bg: #ffffff;--quarto-scss-export-pagination-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-pagination-focus-color: rgb(10.4, 88, 202.4);--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: rgb(10.4, 88, 202.4);--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-pagination-active-color: #ffffff;--quarto-scss-export-pagination-active-bg: #0d6efd;--quarto-scss-export-pagination-active-border-color: #0d6efd;--quarto-scss-export-pagination-disabled-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-bg: rgba(33, 37, 41, 0.03);--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #ffffff;--quarto-scss-export-accordion-color: #212529;--quarto-scss-export-accordion-bg: #ffffff;--quarto-scss-export-accordion-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-accordion-button-color: #212529;--quarto-scss-export-accordion-button-bg: #ffffff;--quarto-scss-export-accordion-button-active-bg: rgb(206.6, 226, 254.6);--quarto-scss-export-accordion-button-active-color: rgb(5.2, 44, 101.2);--quarto-scss-export-accordion-button-focus-border-color: rgb(134, 182.5, 254);--quarto-scss-export-accordion-icon-color: #212529;--quarto-scss-export-accordion-icon-active-color: rgb(5.2, 44, 101.2);--quarto-scss-export-tooltip-color: #ffffff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #212529;--quarto-scss-export-popover-arrow-color: #ffffff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #ffffff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #ffffff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #ffffff;--quarto-scss-export-progress-bar-bg: #0d6efd;--quarto-scss-export-list-group-color: #212529;--quarto-scss-export-list-group-bg: #ffffff;--quarto-scss-export-list-group-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #0d6efd;--quarto-scss-export-list-group-active-color: #ffffff;--quarto-scss-export-list-group-active-border-color: #0d6efd;--quarto-scss-export-list-group-disabled-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-list-group-disabled-bg: #ffffff;--quarto-scss-export-list-group-action-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #212529;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #ffffff;--quarto-scss-export-thumbnail-border-color: rgb(221.7, 222.3, 222.9);--quarto-scss-export-figure-caption-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(33, 37, 41, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #ffffff;--quarto-scss-export-carousel-indicator-active-bg: #ffffff;--quarto-scss-export-carousel-caption-color: #ffffff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #ffffff;--quarto-scss-export-offcanvas-color: #212529;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #ffffff;--quarto-scss-export-kbd-bg: #212529;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 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H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else 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span.wa { + color: #5E5E5E; + font-style: italic; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // dispatch for htmlwidgets + // they use slideenter event to trigger resize + function fireSlideEnter() { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // dispatch for shiny + // they use BS shown and hidden events to trigger rendering + function distpatchShinyEvents(previous, current) { + if (window.jQuery) { + if (previous) { + window.jQuery(previous).trigger("hidden"); + } + if (current) { + window.jQuery(current).trigger("shown"); + } + } + } + + // tabby.js listener: Trigger event for htmlwidget and shiny + document.addEventListener( + "tabby", + function (event) { + fireSlideEnter(); + distpatchShinyEvents(event.detail.previousTab, event.detail.tab); + }, + false + ); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + // Categories search with listing only use path without query + const currentPagePath = offsetAbsoluteUrl( + window.location.origin + window.location.pathname + ); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + const encodedItem = encodeURI(item); + if ( + encodedItem === currentPagePath || + encodedItem === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (const child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +tabsets.init(); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/thoughts/blog-post-political-compass_files/libs/quarto-html/tabsets/tabsets.js b/thoughts/blog-post-political-compass_files/libs/quarto-html/tabsets/tabsets.js new file mode 100644 index 0000000..51345d0 --- /dev/null +++ b/thoughts/blog-post-political-compass_files/libs/quarto-html/tabsets/tabsets.js @@ -0,0 +1,95 @@ +// grouped tabsets + +export function init() { + window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } + }); +} diff --git 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