parent
28b24084f6
commit
0183bbc8a3
@ -0,0 +1,229 @@ |
|||||||
|
--- |
||||||
|
module: analysis/right_wing |
||||||
|
date: 2026-05-26 |
||||||
|
problem_type: best_practice |
||||||
|
tags: |
||||||
|
- overton-window |
||||||
|
- voting-margin |
||||||
|
- mechanism-classification |
||||||
|
- party-differentiation |
||||||
|
- left-wing-response |
||||||
|
- 2d-extremity |
||||||
|
--- |
||||||
|
|
||||||
|
# Analytical Extensions for Overton Window Shift Analysis |
||||||
|
|
||||||
|
## Context |
||||||
|
|
||||||
|
The core methodology in `overton-window-shift-methodology-2026-05-24.md` established the 7-step framework (strict centrist definition, Procrustes-aligned SVD, centrist support fraction, opposition control, extremity stratification, 2D audit). These are prerequisites — this document covers extensions built on that foundation for richer, more defensible analysis. |
||||||
|
|
||||||
|
Domain decomposition (`domain-decomposition-overton-analysis.md`) must also be applied before interpretation to avoid conflating strategic moderation with genuine acceptance expansion. |
||||||
|
|
||||||
|
## Guidance |
||||||
|
|
||||||
|
### 1. Replace Binary Pass Rate with Voting Margin |
||||||
|
|
||||||
|
The Dutch Tweede Kamer passes 96%+ of motions. Pass rate is a structurally useless ceiling-capped metric. **Always compute `voting_margin` as the primary success metric** before drawing conclusions about motion effectiveness. |
||||||
|
|
||||||
|
**How to compute:** |
||||||
|
``` |
||||||
|
margin = (voor - tegen) / (voor + tegen + afwezig) |
||||||
|
``` |
||||||
|
|
||||||
|
This produces a continuous [-1, +1] scale from unanimous rejection to unanimous support. A motion passing 14-1 (margin=+0.87) and one passing 8-7 (margin=+0.07) are both "passed" — the margin exposes the real signal. |
||||||
|
|
||||||
|
**Validation:** Correlate voting margin against centrist support. If Spearman ρ > 0.7, margin captures a meaningful success gradient that pass rate cannot. In the Dutch data, ρ=0.812. |
||||||
|
|
||||||
|
**Implementation:** `analysis/right_wing/voting_margin.py` — reads `motions.voting_results` (per-party JSON), computes per-motion margin, produces quartile-stratified statistics, and computes pre/post Cohen's d. |
||||||
|
|
||||||
|
**Key design decision:** Per-party aggregation (1 party = 1 vote) rather than seat-weighted. This measures *breadth of cross-spectrum support* — exactly the Overton concept — without conflating coalition size effects. |
||||||
|
|
||||||
|
### 2. Coalition Coding Must Split 2024 at July 1 |
||||||
|
|
||||||
|
Treating all 2024 as the Schoof cabinet (PVV/VVD/NSC/BBB) overestimates the coalition effect. The year contains two governments: |
||||||
|
|
||||||
|
- **Jan–Jun 2024:** Rutte IV (VVD/D66/CDA/CU) — caretaker, limited legislative agenda |
||||||
|
- **Jul–Dec 2024:** Schoof I (PVV/VVD/NSC/BBB) — right-wing coalition |
||||||
|
|
||||||
|
**Method:** When filtering opposition-only motions, use submission date (not year) against a date-aware coalition dictionary. Motions from Jan–Jun 2024 whose lead submitter is in the Rutte IV coalition must be filtered as government motions, not opposition. |
||||||
|
|
||||||
|
**Impact:** In the Dutch data, this reclassified 32 PVV/BBB motions from "opposition" to "government," correcting an artificial inflation of the opposition-only shift. Without this fix, the opposition-only Cohen's d overstates the shift by attributing coalition-party motions to opposition. |
||||||
|
|
||||||
|
**Implementation:** Embedded in all opposition-filter scripts (`temporal_trajectory.py`, `causal_timing.py`, `predictive_model.py`) via `COALITION` dict. Scripts that only use year-level filtering contain a fragility note. |
||||||
|
|
||||||
|
### 3. Party Differentiation: Disaggregate Right-Wing Bloc |
||||||
|
|
||||||
|
Right-wing parties are not monolithic. Before attributing any Overton shift to "the right," disaggregate by party to identify the actual driver. |
||||||
|
|
||||||
|
**Required dimensions per party:** |
||||||
|
- Volume (motion count) over time |
||||||
|
- Centrist support over time |
||||||
|
- Material impact over time |
||||||
|
- Pre/post CS delta and volume delta |
||||||
|
|
||||||
|
**Method:** Parse submitter party from motion title prefixes using regex patterns matching Dutch motion conventions (e.g., `"Motie van het lid Wilders ..."`). Use `mp_metadata` table as authority for last-name-to-party mapping. Normalize party names via `_PARTY_NORMALIZE` (e.g., `Groep Markuszower → PVV`). |
||||||
|
|
||||||
|
**What to look for:** |
||||||
|
- A party whose centrist support rose *without* filing more motions: not the driver |
||||||
|
- A party whose volume rose *without* CS increase: also not the driver |
||||||
|
- A party with both volume AND CS gains: the primary driver |
||||||
|
- A party that entered government (reduced motions, milder content): follower, not leader |
||||||
|
|
||||||
|
**Surprising finding from Dutch data:** PVV (largest right-wing party) moderated significantly after entering government — *fewer, milder motions*. JA21 (smaller opposition party) was the primary driver of both volume and support gains. The aggregate "right-wing shift" narrative masks a succession story: PVV moderated, JA21 filled the vacated extreme space. |
||||||
|
|
||||||
|
**Implementation:** `analysis/right_wing/party_differentiation.py` — parses submitter parties, builds yearly aggregates, produces 4-panel figure (volume × CS × material impact × pre/post bars). |
||||||
|
|
||||||
|
### 4. Left-Wing Response Analysis: Verify Asymmetry |
||||||
|
|
||||||
|
An Overton window shift is not symmetric. Rising centrist support for right-wing motions could theoretically be driven by left-wing parties hardening their opposition (making centrist support appear to rise relative to a higher denominator). Always verify the left-wing side by computing centrist support for *left-wing* motions. |
||||||
|
|
||||||
|
**Method:** |
||||||
|
1. Identify left-wing motions using the same submitter parsing approach |
||||||
|
2. Compute centrist support for left motions pre/post |
||||||
|
3. Compare Cohen's d for centrist → right shift vs centrist → left shift |
||||||
|
4. Compute per-party left-wing softening scores (Δ in CS for each left party) |
||||||
|
|
||||||
|
**Interpretation guard:** If the centrist shift toward right-wing motions is 10×+ larger than any left-wing hardening, the asymmetry confirms the shift is genuine centrist acceptance, not artifact of denominator change. If both sides shifted, investigate coalition mechanics. |
||||||
|
|
||||||
|
**Dutch finding:** Centrist shift toward right (d=+1.89) was 18.3× larger than left-wing hardening (d=−0.75). Left-wing opposition support barely moved (21.3%→20.2%). Volt was the only left party that softened (+12.9pp). This extreme asymmetry eliminates denominator artifacts as alternative explanations. |
||||||
|
|
||||||
|
**Implementation:** `analysis/right_wing/left_wing_response.py` — loads motions with `CANONICAL_LEFT` submitter filtering, computes both centrist support and left-party support for right-wing motions, produces asymmetry figures. |
||||||
|
|
||||||
|
### 5. Mechanism Validation with Second Classifier |
||||||
|
|
||||||
|
LLM-based mechanism classifications are inherently subjective. Always validate inter-rater reliability before drawing conclusions from mechanism distributions. |
||||||
|
|
||||||
|
**Method:** |
||||||
|
1. Classify motion mechanism with primary classifier (inline subagent, reading full text) |
||||||
|
2. Run independent second classifier with a **different** prompt: |
||||||
|
- Use English wording (vs Dutch for original) |
||||||
|
- Present mechanisms in **reversed** order |
||||||
|
- Include explicit definitions for each mechanism |
||||||
|
- Require strict JSON output with confidence scores |
||||||
|
3. Compute Cohen's κ on overlapping classifications |
||||||
|
4. For disagreements, prefer the classification with confidence ≥ 4, otherwise retain original |
||||||
|
|
||||||
|
**Agreement thresholds:** |
||||||
|
- κ < 0.2: taxonomy is unusable, scrap and rebuild |
||||||
|
- 0.2 ≤ κ < 0.4: taxonomy needs major revision |
||||||
|
- 0.4 ≤ κ < 0.6: taxonomy needs targeted refinement (merge ambiguous pairs) |
||||||
|
- κ ≥ 0.6: taxonomy is sufficiently reliable |
||||||
|
|
||||||
|
**When κ is moderate (0.4–0.6):** Analyze the disagreement pairs. In the Dutch data, the primary confusion was `institutional_rule_of_law ↔ targeted_restriction` — many motions addressing migration enforcement could be classified as either. This revealed a taxonomy ambiguity, not a classification failure: institutional and targeted mechanisms can coexist in the same motion. The fix is not to discard the taxonomy but to note this ambiguity and consider a multi-label classification approach for future iterations. |
||||||
|
|
||||||
|
**Implementation:** `analysis/right_wing/mechanism_validation.py` — runs second classifier via parallel LLM calls, computes κ, confusion matrix, resolves disagreements, generates validated distribution. |
||||||
|
|
||||||
|
### 6. Predictive Model: Feature Importance, Not Just Accuracy |
||||||
|
|
||||||
|
A predictive model's primary value in this domain is not prediction but feature importance — identifying which motion characteristics drive centrist acceptance. |
||||||
|
|
||||||
|
**Method:** |
||||||
|
1. Build features: submitter party (one-hot), category, 2D extremity scores, year, word count, sentiment, coalition membership |
||||||
|
2. Target: high centrist support (CS > 0.5) as binary classification |
||||||
|
3. Train logistic regression (interpretable coefficients) and random forest (captures non-linearities) |
||||||
|
4. Run stratified 5-fold cross-validation with AUC-ROC as primary metric |
||||||
|
5. Report top-10 feature importance rankings from both models |
||||||
|
|
||||||
|
**What models cannot do:** Predict individual motion outcomes in a new parliament. Coalition dynamics, the specific political moment, and the text of the motion interact in ways no tabular model captures. The models validate that the factors we *hypothesize* as important (party, extremity, category) are indeed predictive — this is methodological triangulation, not forecasting. |
||||||
|
|
||||||
|
**Dutch finding:** AUC-ROC=0.81 (logistic), 0.84 (RF). Top predictors: submitter party (FVD=−1.33 → hard to get support), SGP (+0.99 → easy), category (asylum/migration hardest), and `stijl_extremiteit` (higher stylistic extremity predicts lower support). These coefficients validate the analytical framework. |
||||||
|
|
||||||
|
**Implementation:** `analysis/right_wing/predictive_model.py` — sklearn logistic regression + random forest, cross-validated, with SHAP-style coefficient interpretation. |
||||||
|
|
||||||
|
### 7. SVD Spatial Divergence: Center of Gravity Trajectories |
||||||
|
|
||||||
|
The core methodology established Procrustes-aligned SVD drift. The extension decomposes this into group-level center-of-gravity trajectories to measure *divergence*. |
||||||
|
|
||||||
|
**Method:** |
||||||
|
1. Compute Procrustes-aligned party coordinates per window (from core methodology) |
||||||
|
2. For each window, compute the center of gravity for the centrist bloc and right-wing bloc as the component-wise mean |
||||||
|
3. Plot both trajectories on the same axes, with year labels |
||||||
|
4. Compute Euclidean distance between the two centers of gravity at each window |
||||||
|
5. Test for trend: is the inter-bloc distance increasing? |
||||||
|
|
||||||
|
**What divergence means:** If centrists moved LEFT while right-wing stayed put or moved right, the distance between groups grew. This is acceptance without conversion — the Overton window widened without centrists converting to right-wing positions. |
||||||
|
|
||||||
|
**Implementation:** `analysis/right_wing/svd_trajectory_viz.py` — two-panel figure showing individual party arrow trajectories and group center-of-gravity movement. |
||||||
|
|
||||||
|
### 8. Temporal Trajectory: Quarterly Granularity |
||||||
|
|
||||||
|
Binary pre/post analysis is too coarse to distinguish an electoral shock from a gradual trend. Always decompose into quarters. |
||||||
|
|
||||||
|
**Method:** |
||||||
|
1. Assign each motion to a quarter based on submission date |
||||||
|
2. Compute quarterly mean centrist support, with 95% CI |
||||||
|
3. Compute quarter-over-quarter Cohen's d to identify the largest single-step change |
||||||
|
4. Run a breakpoint detection method (e.g., PELT or binary segmentation) to find structural changes |
||||||
|
5. Annotate with known political events (elections, coalition changes, EU-level shifts) |
||||||
|
|
||||||
|
**What to look for:** |
||||||
|
- Immediate jump: the shift was an electoral/political event, not a trend |
||||||
|
- Gradual ramp: the shift was a cultural/social trend, harder to attribute to specific events |
||||||
|
- Peak and revert: if support peaked and returned to baseline, the shift may be temporary |
||||||
|
|
||||||
|
**Dutch finding:** The shift was an immediate electoral jump (2023-Q4→2024-Q1: +0.18), suggesting the right-wing electoral victory, not a gradual normalization process. The peak at 2024-Q4 (0.648) followed by reversion to 0.334 by 2026-Q1 raises the possibility of a temporary "coalition honeymoon" rather than permanent shift. |
||||||
|
|
||||||
|
**Implementation:** `analysis/right_wing/temporal_trajectory.py` — quarterly binning from submission dates, computes rolling statistics, CIs, and event annotations. `causal_timing.py` adds political event overlay and breakpoint detection. |
||||||
|
|
||||||
|
### 9. 2D Extremity Divergence: Separate Stylistic from Material |
||||||
|
|
||||||
|
Single-dimension extremity scores masked diverging trends. Always decompose into stylistic and material dimensions. |
||||||
|
|
||||||
|
**Method:** |
||||||
|
1. Use the two-dimensional rescoring pipeline from the core methodology to score both `stijl_extremiteit` and `materiele_impact` |
||||||
|
2. Compute yearly means separately for each dimension |
||||||
|
3. Test for divergence: are the two dimensions moving in opposite directions? |
||||||
|
4. Compute paired Wilcoxon signed-rank test between pre and post periods for each dimension |
||||||
|
5. If significant divergence is found, single-score trend analysis is structurally misleading |
||||||
|
|
||||||
|
**When to suspect divergence:** If the single-score trend is flat (d≈0) but the underlying dimensions are correlated with centrist support in opposite directions, the flat trend masks real change. A decrease in material impact (motions got substantively milder) can cancel out an increase in stylistic extremity (language got sharper), producing a misleading "no change" signal. |
||||||
|
|
||||||
|
**Dutch finding:** Material impact decreased (−0.146) while stylistic extremity increased (+0.097), with significant Wilcoxon (p=0.002). The single-score "flat trend" was an artifact of these cancelling directions. |
||||||
|
|
||||||
|
**Implementation:** `analysis/right_wing/extremity_2d_temporal.py` — joins `extremity_scores_2d` with `right_wing_motions`, computes per-dimension yearly means, runs Wilcoxon, generates dual-axis time series figure. |
||||||
|
|
||||||
|
### 10. Mechanism Classification: Stratified Sampling with Chi-Squared |
||||||
|
|
||||||
|
When testing whether a specific mechanism (e.g., consensus framing) drives centrist support, use stratified sampling and formal hypothesis testing. |
||||||
|
|
||||||
|
**Method:** |
||||||
|
1. Stratify the motion population into 2×2 buckets: period (pre/post) × support level (high/low CS) |
||||||
|
2. Sample deterministically (fixed motion IDs) for reproducibility |
||||||
|
3. Classify mechanism through manual/subagent review of full title + body text |
||||||
|
4. Test H0: mechanism distribution is independent of support level (chi-squared contingency test) |
||||||
|
5. Test H0: consensus framing is equally common in high-CS and low-CS post-2024 motions (2×2 Fisher/chi-squared) |
||||||
|
|
||||||
|
**Support threshold:** CS > 0.5 for "high" support. This threshold is arbitrary but produces clear separation between mechanisms that appeal broadly (consensus framing) vs those that polarize (targeted restriction, system dismantling). |
||||||
|
|
||||||
|
**When to reject the taxonomy:** If κ < 0.4 from independent validation, the mechanism labels themselves are unreliable and chi-squared results are misleading. In this case, report κ rather than mechanism findings. |
||||||
|
|
||||||
|
**Dutch finding:** Consensus framing accounts for 24% of high-CS post-2024 motions vs 8% of low-CS (significant). This validates the mechanism hypothesis even with moderate κ=0.41 — the signal survives the noise. |
||||||
|
|
||||||
|
**Implementation:** `analysis/right_wing/mechanism_classification.py` — deterministic sample of 200 motions, inline classifications, chi-squared tests, generated report. |
||||||
|
|
||||||
|
## Why This Matters |
||||||
|
|
||||||
|
Without these extensions, Overton window analysis produces results that are correct in direction but misleading in detail: |
||||||
|
- Binary pass rate creates false nulls (no detectable change) |
||||||
|
- Undifferentiated "right-wing" analysis misattributes effects to the wrong party |
||||||
|
- Missing left-wing verification cannot rule out denominator artifacts |
||||||
|
- Single-dimension extremity masks opposite trajectories in stylistic vs material content |
||||||
|
- Missing mechanism validation overstates confidence in classification-based findings |
||||||
|
- Annual aggregation cannot distinguish shock vs trend |
||||||
|
|
||||||
|
## When to Apply |
||||||
|
|
||||||
|
- When pass rate exceeds 90% and provides no signal (always in multi-party parliaments with high passage rates) |
||||||
|
- When right-wing parties have heterogeneous trajectories — some entering government, some remaining in opposition |
||||||
|
- When the analysis must rule out left-wing denominator effects as alternative explanations |
||||||
|
- When LLM-scored extremity metrics are used as independent variables |
||||||
|
- When mechanism classification forms part of the explanatory framework |
||||||
|
- When temporal resolution matters: quarterly decomposition is necessary to distinguish electoral shock from gradual shift |
||||||
|
- When 2D extremity data is available and single-score trends appear flat |
||||||
|
|
||||||
|
## Related |
||||||
|
|
||||||
|
- `overton-window-shift-methodology-2026-05-24.md` — core 7-step methodology (prerequisite) |
||||||
|
- `domain-decomposition-overton-analysis.md` — per-domain decomposition (apply before interpretation) |
||||||
|
- `svd-labels-voting-patterns-not-semantics.md` — foundational: SVD captures voting patterns |
||||||
|
- `.opencode/skills/score-extremity/SKILL.md` — two-dimensional extremity scoring pipeline |
||||||
Loading…
Reference in new issue