From 3a240fd907413687d49d47bb1bc4c451192ccc79 Mon Sep 17 00:00:00 2001 From: Sven Geboers Date: Thu, 16 Apr 2026 18:47:43 +0200 Subject: [PATCH] docs(blog): update political compass post with correct EVR, GL-PvdA evidence, scree plot, HTML table - Fix EVR numbers: PC1~29%, PC2~11.5% (~41%) single-window; PC1~14.6%, PC2~13.1% multi-window - Fix window count: 38 -> 41 time windows - Add scree plot (docs/research/scree_multiwindow.png) embedded in EVR callout - Add party agreement heatmap (docs/research/party_agreement_2023Q3.png) with GL-PvdA 99.8% figure - Convert markdown pipe-table to HTML table - Remove text-embedding/fused pipeline references (not in production) - Simplify pipeline diagram and reproducibility block - Update DB size to ~18 GB --- docs/research/party_agreement_2023Q3.csv | 23 + docs/research/party_agreement_2023Q3.png | Bin 0 -> 184955 bytes docs/research/party_agreement_2023Q3.svg | 6288 +++++++++++++++++ docs/research/scree_multiwindow.png | Bin 0 -> 32672 bytes docs/research/scree_multiwindow.svg | 1407 ++++ thoughts/blog-post-political-compass.html | 251 +- ...16-political-compass-blog-update-design.md | 153 + 7 files changed, 8038 insertions(+), 84 deletions(-) create mode 100644 docs/research/party_agreement_2023Q3.csv create mode 100644 docs/research/party_agreement_2023Q3.png create mode 100644 docs/research/party_agreement_2023Q3.svg create mode 100644 docs/research/scree_multiwindow.png create mode 100644 docs/research/scree_multiwindow.svg create mode 100644 thoughts/shared/designs/2026-04-16-political-compass-blog-update-design.md diff --git a/docs/research/party_agreement_2023Q3.csv b/docs/research/party_agreement_2023Q3.csv new file mode 100644 index 0000000..394c20b --- /dev/null +++ b/docs/research/party_agreement_2023Q3.csv @@ -0,0 +1,23 @@ +,BBB,BIJ1,Brinkman,CDA,ChristenUnie,D66,DENK,Ephraim,FVD,Fractie Den Haan,GroenLinks,Groep Van Haga,Gündogan,JA21,Omtzigt,PVV,PvdA,PvdD,SGP,SP,VVD,Volt 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/thoughts/blog-post-political-compass.html b/thoughts/blog-post-political-compass.html index e2b6593..6f7bade 100644 --- a/thoughts/blog-post-political-compass.html +++ b/thoughts/blog-post-political-compass.html @@ -5,115 +5,198 @@ Mapping Dutch Democracy: Building a Political Compass -

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

+

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.

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The Starting Point: Open Data, Hidden Structure

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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.

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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.

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+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. +
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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.

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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:

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  • 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.

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    +
  • Rows: individual MPs (and party actors for collective votes)
    • +
  • Columns: motions
    • +
  • Values: +1 (voor), −1 (tegen), 0 (absent/abstain)
    • +
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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.

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Making Windows Comparable: Procrustes Alignment

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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.

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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:

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

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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.

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Step 2: What Each Motion Is Actually About

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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.

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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. Configuration: prefer OPENROUTER_API_KEY and fall back to OPENAI_API_KEY where appropriate.

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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.

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Step 3: Fused Embeddings — The Best of Both Worlds

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

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

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

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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.

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Step 2: Finding Similar Motions

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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.

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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.

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The Numbers: What We're Working With

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After the full pipeline run:

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| Year | Motions | -|------|---------| -| 2016 | 132 | -| 2017 | 30 | -| 2018 | 100 | -| 2019 | 3,374 | -| 2020 | 4,228 | -| 2021 | 4,289 | -| 2022 | 4,116 | -| 2023 | 3,272 | -| 2024 | 3,968 | -| 2025 | 3,715 | -| 2026 | 948 | -| Total | 28,172 |

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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.

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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.

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The similarity cache holds 405,216 precomputed pairs — top 10 neighbors per motion per window — making lookup instant at query time.

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Interesting Findings

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The 2022–2023 Polarization Surge

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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.

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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.

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BBB's Geometric Arrival

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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.

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The Strange Case of "Verworpen."

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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.

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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.

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Party Cohesion as a Signal

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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.

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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.

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+ + + + + + + + + + + + + + + + +
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.

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

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+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 +
+ +

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.

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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.

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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.

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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.

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Finding 2: When Left and Right Unite Against the Center

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+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.

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The topics tell the story:

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  • 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.
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  • Respite care for intensive caregivers — same coalition of radical left and radical right, opposing centrist fiscal restraint.
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  • Anti-fraud budget retention — the coalition wanted to maintain the anti-fraud apparatus (think: the toeslagenaffaire aftermath); both flanks pushed back.
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  • Education funding — motions to increase fundamental education budgets. VVD and D66 voted against; PVV and SP voted together.
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  • 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.

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Finding 3: BBB's Geometric Arrival

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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.

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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.

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Finding 4: The Closest Votes in a Decade

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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:

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  • Family reunification for AMV status holders (Boomsma motion, 2025) — immigration policy at its most contested
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  • Nuclear weapons and NATO (Dobbe motion, 2025) — whether to push for nuclear disarmament within the alliance
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  • Long COVID research funding (Kostic motion, 2025) — healthcare commitments that split parties along unexpected lines
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  • Cormorant population management (Kostic motion, 2025) — agricultural vs. ecological interests in a literal bird-counting exercise
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+ +

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.

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The Pipeline Architecture

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

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API (Tweede Kamer OData) 
-  → download_past_year.py 
-  → motions table (28,172 rows)
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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)

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svd_vectors + embeddings
-  → fusion.py                → fused_embeddings table (40,522 rows)

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fused_embeddings
-  → similarity/compute.py    → similarity_cache table (405,216 rows, top-10 per window)

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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.

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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.

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+
API (Tweede Kamer OData)
+  → download_past_year.py        → motions table (28,304 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 and semantically similar ones from across the decade. Trace how a policy debate evolved — who championed it, how the coalitions shifted.

-

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.

-

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.

-

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.

-

---

+ +

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.

+ +

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.

+ +

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

+ +

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

-
bash
-
Download historical data

+
# 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)

+
+# 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

+
+# Enrich with full motion body text
 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.

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

+
 
Democracy is more legible than it looks.

 
 
diff --git a/thoughts/shared/designs/2026-04-16-political-compass-blog-update-design.md b/thoughts/shared/designs/2026-04-16-political-compass-blog-update-design.md
new file mode 100644
index 0000000..48570ab
--- /dev/null
+++ b/thoughts/shared/designs/2026-04-16-political-compass-blog-update-design.md
@@ -0,0 +1,153 @@
+---
+date: 2026-04-16
+topic: "political-compass-blog-update"
+status: draft
+---
+
+## Problem Statement
+
+We need the "political compass" blog post under thoughts/ to show figures and numbers that exactly match the repository's canonical pipeline outputs. That requires producing reproducible assets (scree plots, party-agreement CSVs and heatmaps) from the codebase, placing them in docs/research, and making minimal edits to the blog HTML to reference those files.
+
+**Key constraint:** All numbers and figures must come from the canonical functions or the authoritative DB (data/motions.db). No invented values.
+
+
+## Constraints
+
+**Non-negotiables:**
+- Use canonical functions (analysis.political_axis.compute_svd_spectrum, analysis.explorer_data.load_scree_data) as data sources.
+- Place generated files under **docs/research/** with reproducible, deterministic filenames.
+- Keep blog edits minimal and reversible: swap the markdown table for an HTML table and insert  and CSV links.
+
+**Operational constraints:**
+- Plotly SVG export requires kaleido; provide a reliable matplotlib fallback.
+- data/motions.db must contain required rows (e.g. singular_values) or we must run compute_svd_spectrum first.
+
+
+## Approach (chosen)
+
+I'm choosing a single, pragmatic approach that balances reproducibility, low-risk changes, and minimal new dependencies:
+
+**Chosen approach:** write a small export script (scripts/export_blog_assets.py) that:
+- Calls **analysis.political_axis.compute_svd_spectrum(db_path)** for the multi-window scree and **analysis.explorer_data.load_scree_data(db_path)** for the current_parliament scree fallback.
+- Re-uses the explorer._render_scree_plot logic (or extracts the Plotly-building code into a helper) to build a Plotly Figure and export SVG via **fig.write_image(..., format='svg')** when kaleido is available.
+- Falls back to matplotlib-based rendering if fig.write_image fails.
+- Computes pairwise party agreement / GL–PvdA trajectory using SQL and the logic from scripts/generate_extra_charts.py, writes CSV with pandas.DataFrame.to_csv(...), and writes a heatmap SVG to docs/research.
+- Writes assets with deterministic filenames into **docs/research/** and prints/returns the exact paths and the key numeric values (EVR% for caption).
+
+Why this approach:
+- It uses the canonical functions already present in the codebase so numbers match UI and tests.
+- Keeps edits limited to a single script and the blog HTML, making review and rollback trivial.
+- Provides a clear fallback for environments without kaleido.
+
+Alternatives considered (brief):
+
+1) Modify existing scripts (scripts/generate_extra_charts.py) to write into docs/research.
+- Pro: reuses plotting code directly.
+- Con: those scripts are opinionated about output layout and write HTML, not SVG/CSV; harder to keep minimal change.
+
+2) Recompute everything via pipeline.run_pipeline and copy pipeline outputs to docs/research.
+- Pro: purely canonical pipeline outputs.
+- Con: heavier — pipeline run may be slow and more intrusive; more environment setup.
+
+I rejected them because the export-script approach is lighter, reproducible, and gives explicit control over filenames and fallbacks.
+
+
+## Architecture
+
+High-level: a small command-line script (scripts/export_blog_assets.py) driven by the canonical DB, the analysis layer, and the visualize helpers.
+
+**Major pieces:**
+- **Exporter script**: orchestrates reads from DB, computes metrics, builds figures, writes CSV/SVG into docs/research.
+- **Canonical analysis functions**: analysis.political_axis and analysis.explorer_data (data source only, no side effects).
+- **Plot builders**: reuse of explorer._render_scree_plot / analysis.visualize helpers to produce Plotly Figure objects.
+- **Fallback renderer**: minimal matplotlib routines producing PNG/SVG if Plotly image export fails.
+- **Blog edit**: minimal HTML changes in thoughts/blog-post-political-compass.html to reference the generated assets.
+
+
+## Components and Responsibilities
+
+**scripts/export_blog_assets.py** (new)
+- Inputs: path to DB (default data/motions.db), optional --window (e.g. 2023Q3 or 'current_parliament'), output directory (default docs/research).
+- Responsibilities:
+  - Run compute_svd_spectrum(db_path) and/or load_scree_data(db_path).
+  - Build scree Plotly figures and export SVGs (multi-window and current_parliament).
+  - Compute party agreement matrices, export CSVs and heatmap SVGs for requested window(s).
+  - Print the EVR numbers and paths for copy into blog captions.
+  - Exit non-zero on fatal errors (missing DB, empty results) with clear messages.
+
+**Explorer / analysis helpers**
+- analysis.political_axis.compute_svd_spectrum(db_path): canonical EVR source for multi-window scree.
+- analysis.explorer_data.load_scree_data(db_path): canonical loader for current_parliament scree (fallback).
+- explorer._render_scree_plot(importances): returns Plotly figure in Streamlit — reuse the building logic to return a Figure for export.
+
+**Fallback renderer**
+- Minimal matplotlib code that takes the EVR vector and draws a bar/scree-like chart and saves as SVG/PNG.
+
+**Blog file edits**
+- thoughts/blog-post-political-compass.html: replace markdown pipe table with an HTML table and insert  and  plus CSV links.
+
+
+## Data Flow
+
+1. Exporter reads data from **data/motions.db**.
+2. Calls compute_svd_spectrum(db_path) to get multi-window EVR arrays.
+3. Calls load_scree_data(db_path) to get 'current_parliament' singular values if available.
+4. Builds Plotly Figures for scree plots (multi-window and current_parliament).
+5. Exports Figures to **docs/research/*.svg** (uses fig.write_image when kaleido is present, otherwise matplotlib fallback).
+6. Computes party agreement matrices via the SQL used in scripts/generate_extra_charts.py, writes CSVs to **docs/research/**.
+7. Writes a party-heatmap SVG to **docs/research/**.
+8. The blog HTML references those files via relative paths (../docs/research/...).
+
+
+## Error Handling Strategy
+
+**Fail early with informative messages.**
+
+- If DB is missing or unreadable: exit with a clear error and suggestion to run the pipeline or point --db to a valid file.
+- If compute_svd_spectrum returns empty / no windows: print guidance to run scripts/recompute_svd.py or pipeline.run_pipeline and exit non-zero.
+- If Plotly image export fails (kaleido missing): log the error, attempt matplotlib fallback, and continue.
+- If CSV or SVG write fails due to IO permissions: log path and permission error and exit non-zero (don't silently drop assets).
+
+All non-fatal warnings are printed with suggested remediation steps.
+
+
+## Testing Strategy
+
+Local verification steps (automated script + manual checks):
+
+- Unit smoke: run scripts/export_blog_assets.py --db data/motions.db --dry-run to verify the functions produce non-empty arrays and print expected output paths.
+- Functional: run the script to produce assets and assert files exist: docs/research/scree_multiwindow.svg, docs/research/scree_current_parliament.svg, docs/research/party_agreement_.csv, docs/research/party_agreement_.svg.
+- Sanity numbers: script prints the top EVR values used in captions. Cross-check printed EVR against explorer UI numbers (run explorer locally if needed).
+- Blog preview: open thoughts/blog-post-political-compass.html in browser (file://) and confirm images render and captions match printed numbers.
+
+Add a basic test under tests/ that runs the exporter against a small fixture DB (or a tmp DB produced from tests/test_political_compass.py fixtures) to assert the script creates at least the CSV and a PNG/SVG.
+
+
+## Effort Estimate & Schedule
+
+- Draft exporter script and fallback renderer: 2–3 hours.
+- Wire up SQL for party agreement and CSV export: 1 hour.
+- Run and verify assets locally (including possible compute_svd if DB missing): 30–60 minutes.
+- Blog HTML edits and quick preview: 30 minutes.
+- Add a minimal test + docs: 1 hour.
+
+Total: ~5–6 hours of focused work (assuming data/motions.db is present and reasonably up-to-date). If compute_svd must be run across many windows or pipeline.run_pipeline is required, add 30–90 minutes.
+
+
+## Risks & Mitigations
+
+- **Missing singular_values row for current_parliament.** Mitigation: script detects and runs compute_svd_spectrum or instructs operator to run scripts/recompute_svd.py.
+- **Kaleido not installed causing fig.write_image to fail.** Mitigation: implement matplotlib fallback and print clear message recommending pip install kaleido.
+- **DB schema drift or missing party ids.** Mitigation: script validates expected tables/columns and fails with actionable message.
+- **Assets not committed to git.** Mitigation: recommend the maintainer commit the generated files; optionally script can print a git add/commit suggestion but must not auto-commit without user request.
+
+
+## Open Questions
+
+- Which specific window id(s) do we want for the GL–PvdA CSV/heatmap? (I'll default to 'current_parliament' and allow an explicit --window flag.)
+- Should the script auto-commit generated assets to git, or should it stop and ask human to commit? (I recommend manual commit.)
+
+
+---
+
+I'm proceeding to create the design doc. Interrupt if you want changes.