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<h1>Mapping Dutch Democracy: Building a Political Compass from 28,000 Parliamentary Votes</h1>
<p><em>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?</em></p>
<p>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.</p>
<h2>The Starting Point: Open Data, Hidden Structure</h2>
<p>The Dutch Parliament publishes every vote — every <em>motie</em>, every <em>amendement</em>, every <em>besluit</em> — in an open OData API. We're talking over <strong>28,000 distinct motions</strong> spanning 2016 to 2026, each with a record of how every individual MP voted: <em>voor</em> (for), <em>tegen</em> (against), <em>onthouden</em> (abstained), or <em>afwezig</em> (absent). That's over 500,000 individual vote records.</p>
<body class="fullcontent quarto-light">
<div class="callout">
<strong>A note on the numbers:</strong> 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.
<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
<main class="content" id="quarto-document-content">
<header id="title-block-header" class="quarto-title-block default">
<div class="quarto-title">
<h1 class="title">Mapping Dutch Democracy: Building a Political Compass from 29,000+ Parliamentary Votes</h1>
</div>
<p>This is an extraordinary dataset. But in raw form it's just a table of votes. The interesting question is: can we extract <em>structure</em> — left vs. right, progressive vs. conservative, governing vs. opposition — purely from the pattern of who votes with whom?</p>
<p>The answer is yes, and the method is surprisingly elegant.</p>
<h2>Step 1: Turning Votes into Geometry</h2>
<div class="quarto-title-meta">
<div>
<div class="quarto-title-meta-heading">Author</div>
<div class="quarto-title-meta-contents">
<p>Stemwijzer Analysis </p>
</div>
</div>
<div>
<div class="quarto-title-meta-heading">Published</div>
<div class="quarto-title-meta-contents">
<p class="date">June 16, 2026</p>
</div>
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</div>
</header>
<p><em>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?</em></p>
<p>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.</p>
<hr>
<section id="the-starting-point-open-data-hidden-structure" class="level2">
<h2 class="anchored" data-anchor-id="the-starting-point-open-data-hidden-structure">The Starting Point: Open Data, Hidden Structure</h2>
<p>The Dutch Parliament publishes every vote — every <em>motie</em>, every <em>amendement</em>, every <em>besluit</em> — in an open OData API. We’re talking over <strong>29,500 motions</strong> spanning 2016 to 2026, with a record of how every individual MP voted: <em>voor</em> (for), <em>tegen</em> (against), <em>onthouden</em> (abstained), or <em>afwezig</em> (absent). That’s 531,000 individual vote records.</p>
<p>This is an extraordinary dataset. But in raw form it’s just a table of votes. The interesting question is: can we extract <em>structure</em> — left vs.&nbsp;right, progressive vs.&nbsp;conservative, governing vs.&nbsp;opposition — purely from the pattern of who votes with whom?</p>
<p>The answer is yes, and the method is surprisingly elegant.</p>
<hr>
</section>
<section id="step-1-turning-votes-into-geometry" class="level2">
<h2 class="anchored" data-anchor-id="step-1-turning-votes-into-geometry">Step 1: Turning Votes into Geometry</h2>
<p>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.</p>
<p>I represent this with <strong>Singular Value Decomposition (SVD)</strong> on the MP × motion matrix:</p>
<ul>
<li>Rows: individual MPs (and party actors for collective votes)</li>
<li>Columns: motions</li>
<li>Values: +1 (voor), −1 (tegen), 0 (absent/abstain)</li>
<li>Values: +1 (voor), -1 (tegen), 0 (absent/abstain)</li>
</ul>
<p>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: <strong>the axes emerge from the math, not from any labeling on my part.</strong></p>
<h3>Making Windows Comparable: Procrustes Alignment</h3>
<p>Running SVD independently per time window creates a subtle problem: SVD axes are <strong>arbitrarily oriented</strong>. 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.</p>
<p>I request 50 SVD dimensions per window — but the actual dimensionality is constrained by <code>min(n_MPs, n_motions) - 1</code>. Sparse windows (early years, partial quarters) produce fewer meaningful dimensions. The pipeline handles this gracefully, storing whatever <code>k_used</code> is for each window so downstream fusion always works with the actual vector length.</p>
<section id="making-windows-comparable-procrustes-alignment" class="level3">
<h3 class="anchored" data-anchor-id="making-windows-comparable-procrustes-alignment">Making Windows Comparable: Procrustes Alignment</h3>
<p>Running SVD independently per window creates a subtle problem: SVD axes are <strong>arbitrarily oriented</strong>. 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.</p>
<p>The fix is <strong>Procrustes alignment</strong>: 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:</p>
<pre><code>R = argmin_R ||A − B @ R||_F, subject to R'R = I</code></pre>
<pre><code>R = argmin_R ||A - B @ R||_F, subject to R'R = I</code></pre>
<p>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.</p>
<h2>Step 2: Finding Similar Motions</h2>
<p>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.</p>
<p>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.</p>
<h2>The Numbers: What We're Working With</h2>
<table>
<thead><tr><th>Year</th><th>Motions</th><th>Breakdown</th></tr></thead>
<p>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.</p>
<hr>
</section>
</section>
<section id="step-2-what-each-motion-is-actually-about" class="level2">
<h2 class="anchored" data-anchor-id="step-2-what-each-motion-is-actually-about">Step 2: What Each Motion Is Actually About</h2>
<p>Voting patterns tell us <em>who</em> agrees, but not <em>why</em>. For that, I add <strong>text embeddings</strong> — dense vector representations of each motion’s content using a language model.</p>
<p>I use <strong><code>qwen/qwen3-embedding-4b</code></strong> 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.</p>
<p>This lets us do something powerful: find motions that are genuinely similar in <em>topic</em>, 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.</p>
<hr>
</section>
<section id="step-3-fused-embeddings-the-best-of-both-worlds" class="level2">
<h2 class="anchored" data-anchor-id="step-3-fused-embeddings-the-best-of-both-worlds">Step 3: Fused Embeddings — The Best of Both Worlds</h2>
<p>SVD gives the political-structural signal: <em>how does this motion split the chamber?</em> Text embeddings give the semantic signal: <em>what is this motion about?</em></p>
<p>I concatenate both into a <strong>fused vector</strong> per motion per window:</p>
<pre><code>fused = [svd_dims (typically 50)] + [text_dims (2560)] = typically 2610 dimensions</code></pre>
<p>The actual dimension varies slightly because SVD dimensionality adapts to window density — the code stores <code>svd_dims</code> and <code>text_dims</code> per row so nothing downstream has to assume a fixed size.</p>
<p>This fused representation powers the similarity search. Two motions are “close” only if they’re about a similar <em>topic</em> <strong>and</strong> they produce a similar <em>political split</em>. 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.</p>
<hr>
</section>
<section id="the-numbers-what-were-working-with" class="level2">
<h2 class="anchored" data-anchor-id="the-numbers-what-were-working-with">The Numbers: What We’re Working With</h2>
<p>After the full pipeline run:</p>
<table class="caption-top table">
<thead>
<tr class="header">
<th>Year</th>
<th>Motions</th>
</tr>
</thead>
<tbody>
<tr><td>2016</td><td>162</td><td>Mostly legislative proposals (data incomplete)</td></tr>
<tr><td>2017</td><td>126</td><td>Mostly legislative proposals (data incomplete)</td></tr>
<tr><td>2018</td><td>124</td><td>Mostly legislative proposals (data incomplete)</td></tr>
<tr><td>2019</td><td>3,374</td><td>2,058 moties + 350 amendementen</td></tr>
<tr><td>2020</td><td>4,223</td><td>3,141 moties + 354 amendementen</td></tr>
<tr><td>2021</td><td>4,283</td><td>3,395 moties + 236 amendementen</td></tr>
<tr><td>2022</td><td>4,115</td><td>3,255 moties + 290 amendementen</td></tr>
<tr><td>2023</td><td>3,272</td><td>2,557 moties + 217 amendementen</td></tr>
<tr><td>2024</td><td>3,965</td><td>3,007 moties + 359 amendementen</td></tr>
<tr><td>2025</td><td>3,712</td><td>2,900 moties + 251 amendementen</td></tr>
<tr><td>2026</td><td>948</td><td>849 moties + 21 amendementen (partial year)</td></tr>
<tr class="odd">
<td>2016</td>
<td>162</td>
</tr>
<tr class="even">
<td>2017</td>
<td>126</td>
</tr>
<tr class="odd">
<td>2018</td>
<td>124</td>
</tr>
<tr class="even">
<td>2019</td>
<td>3,374</td>
</tr>
<tr class="odd">
<td>2020</td>
<td>4,223</td>
</tr>
<tr class="even">
<td>2021</td>
<td>4,283</td>
</tr>
<tr class="odd">
<td>2022</td>
<td>4,115</td>
</tr>
<tr class="even">
<td>2023</td>
<td>3,272</td>
</tr>
<tr class="odd">
<td>2024</td>
<td>3,965</td>
</tr>
<tr class="even">
<td>2025</td>
<td>3,712</td>
</tr>
<tr class="odd">
<td>2026</td>
<td>2,214</td>
</tr>
<tr class="even">
<td><strong>Total</strong></td>
<td><strong>29,570</strong></td>
</tr>
</tbody>
</table>
<p>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.</p>
<div class="callout">
<strong>The 2022 spike is striking.</strong> 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.
</div>
<h2>Finding 1: The Merger That Was Already Written in the Votes</h2>
<div class="finding">
<strong>The GroenLinks–PvdA merger wasn't a surprise to the data.</strong> 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 <strong>99.8%</strong> of recorded votes.
<img src="../docs/research/party_agreement_2023Q3.png" alt="Party agreement matrix — 2023-Q3" style="width:100%;max-width:700px;border-radius:8px;margin:12px 0;display:block">
</div>
<p>The raw data preserves the distinction carefully. From 2019 through mid-2023, the <code>svd_vectors</code> table lists <strong>GroenLinks</strong> and <strong>PvdA</strong> as separate entries per window. From late 2023 onwards — when the merger formally took effect in parliament — a single <strong>GroenLinks-PvdA</strong> 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.</p>
<p>What's striking is <em>how early</em> 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.</p>
<p>This created an interesting pipeline challenge: the party normalization step has a mapping that folds both names into <code>GroenLinks-PvdA</code> across the <em>entire</em> 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.</p>
<p>After the formal merger, GroenLinks-PvdA became one of the most <strong>cohesive</strong> 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.</p>
<h2>Finding 2: When Left and Right Unite Against the Center</h2>
<div class="finding">
<strong>The most surprising pattern in the data isn't left vs. right — it's left <em>and</em> right vs. the governing coalition.</strong>
</div>
<p>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) <strong>against</strong> the governing parties VVD, D66, CDA, and ChristenUnie. This isn't a one-off — it happened on dozens of motions.</p>
<p>The topics tell the story:</p>
<ul>
<li><strong>Disability care bureaucracy</strong> — motions to reduce administrative burden in disability care. The populist right and the progressive left both opposed the coalition's market-oriented approach.</li>
<li><strong>Respite care for intensive caregivers</strong> — same coalition of radical left and radical right, opposing centrist fiscal restraint.</li>
<li><strong>Anti-fraud budget retention</strong> — the coalition wanted to maintain the anti-fraud apparatus (think: the toeslagenaffaire aftermath); both flanks pushed back.</li>
<li><strong>Education funding</strong> — motions to increase fundamental education budgets. VVD and D66 voted against; PVV and SP voted together.</li>
<li><strong>Regional infrastructure</strong> — train stations, Eindhoven connectivity, regional investment. Left+right voted for; coalition voted against.</li>
</ul>
<p>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.</p>
<h2>Finding 3: BBB's Geometric Arrival</h2>
<p>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.</p>
<p>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.</p>
<h2>Finding 4: The Closest Votes in a Decade</h2>
<p>The controversy score (<code>1 − winning_margin</code>) reveals the knife-edge votes. In the current fragmented parliament, the tightest split is a perfect <strong>8–8 party-line tie</strong> — decided by the chamber chair's casting vote. These happened on:</p>
<ul>
<li><strong>Family reunification for AMV status holders</strong> (Boomsma motion, 2025) — immigration policy at its most contested</li>
<li><strong>Nuclear weapons and NATO</strong> (Dobbe motion, 2025) — whether to push for nuclear disarmament within the alliance</li>
<li><strong>Long COVID research funding</strong> (Kostic motion, 2025) — healthcare commitments that split parties along unexpected lines</li>
<li><strong>Cormorant population management</strong> (Kostic motion, 2025) — agricultural vs. ecological interests in a literal bird-counting exercise</li>
</ul>
<p>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.</p>
<p>More broadly, over <strong>15,000 motions</strong> 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.</p>
<h2>The Pipeline Architecture</h2>
<p>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.</p>
<p>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.</p>
<p>The similarity cache holds <strong>409,938 precomputed pairs</strong> — top 10 neighbors per motion per window — making lookup instant at query time.</p>
<hr>
</section>
<section id="interesting-findings" class="level2">
<h2 class="anchored" data-anchor-id="interesting-findings">Interesting Findings</h2>
<section id="the-20222023-polarization-surge" class="level3">
<h3 class="anchored" data-anchor-id="the-20222023-polarization-surge">The 2022–2023 Polarization Surge</h3>
<p>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.</p>
<p>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.</p>
</section>
<section id="bbbs-geometric-arrival" class="level3">
<h3 class="anchored" data-anchor-id="bbbs-geometric-arrival">BBB’s Geometric Arrival</h3>
<p>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.</p>
</section>
<section id="the-strange-case-of-verworpen." class="level3">
<h3 class="anchored" data-anchor-id="the-strange-case-of-verworpen.">The Strange Case of “Verworpen.”</h3>
<p>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 <strong>identical</strong> — cosine similarity 1.0 to every other “Verworpen.” in the cache. Technically correct; semantically meaningless. The UI layer filters these out.</p>
<p>It’s a reminder that <strong>data quality surprises emerge at scale</strong>. I found three or four similar pathologies (motions withdrawn mid-session, duplicate API records) that required explicit handling.</p>
</section>
<section id="party-cohesion-as-a-signal" class="level3">
<h3 class="anchored" data-anchor-id="party-cohesion-as-a-signal">Party Cohesion as a Signal</h3>
<p>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.</p>
<p>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.</p>
<hr>
</section>
</section>
<section id="the-pipeline-architecture" class="level2">
<h2 class="anchored" data-anchor-id="the-pipeline-architecture">The Pipeline Architecture</h2>
<p>Single DuckDB database, modular Python pipeline, no cloud infrastructure:</p>
<pre><code>API (Tweede Kamer OData)
→ download_past_year.py → motions table (28,304 rows)
<pre><code>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)</code></pre>
<p>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.</p>
<p>The database sits at ~18 GB on disk — the full parliamentary text for 26,000+ motions accounts for most of that.</p>
<h2>What the Axes Actually Mean</h2>
<p>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 <em>meaning</em> of each axis has to be derived from the actual motions that load heavily on it.</p>
<p>I solved this by extracting the top 50 motions per component (by absolute loading score), then analyzing their content. Some clear patterns emerged:</p>
<ul>
<li><strong>Component 1</strong>: Fiscal-economic policy vs. social welfare and international rights — the classic left-right split.</li>
<li><strong>Component 2</strong>: Nationalist vs. multilateralist orientation — PVV/FvD on one side, Volt/GroenLinks-PvdA on the other.</li>
<li><strong>Component 3</strong>: 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).</li>
</ul>
<div class="callout">
<strong>How much do the first two axes actually capture?</strong> In a single-window SVD (current parliament), PC1 explains ~29% of the variance and PC2 explains ~11.5% — together accounting for <strong>~41%</strong> 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 <em>all</em> 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.
<img src="../docs/research/scree_multiwindow.png" alt="Scree plot — multi-window Procrustes-aligned SVD" style="width:100%;max-width:600px;border-radius:8px;margin:12px 0;display:block">
<em style="font-size:0.85em">Scree plot across 41 aligned quarterly windows. PC1 = 14.6%, PC2 = 13.1%.</em>
</div>
<h2>What's Next</h2>
<p><strong>Motion explorer</strong>: 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.</p>
<p><strong>Party trajectory animation</strong>: 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.</p>
<p><strong>Cross-party coalition patterns</strong>: Which topics produce unusual coalition configurations — motions where the normal left-right split breaks down and unexpected alliances form.</p>
<p><strong>Cabinet crisis detection</strong>: 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.</p>
<h2>Reproducibility</h2>
<pre><code># 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</code></pre>
<p>All computation — SVD, similarity — runs locally on a single machine. No cloud services, no GPU required.</p>
→ 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)</code></pre>
<p>The similarity computation is pure NumPy: load all fused vectors for a window, pad to uniform length, L2-normalize, compute the full <code>N×N</code> cosine similarity matrix via a single matrix multiply (<code>normalized @ normalized.T</code>), then extract top-k neighbors per row with <code>np.argpartition</code>. For a 4,000-motion quarter, that’s a 4000×4000 matrix operation — fast enough that it’s not worth batching.</p>
<p>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.</p>
<hr>
</section>
<section id="what-i-built-on-top" class="level2">
<h2 class="anchored" data-anchor-id="what-i-built-on-top">What I Built On Top</h2>
<p>The pipeline above is the foundation. Here’s what it now powers:</p>
<p><strong>Overton Window analysis</strong>: 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 <em>left</em> on the SVD compass. The full analysis covers 3,030 classified right-wing motions, 2D extremity scoring, quarterly trajectories, and mechanism classification. <a href="../reports/overton_window/overton_report.html">Read the full report →</a></p>
<p><strong>2D extremity scoring</strong>: 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 <em>both</em> dimensions, not just in tone.</p>
<p><strong>Streamlit Explorer</strong>: 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.</p>
<hr>
</section>
<section id="reproducibility" class="level2">
<h2 class="anchored" data-anchor-id="reproducibility">Reproducibility</h2>
<div class="code-copy-outer-scaffold"><div class="sourceCode" id="cb4"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Download historical data</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="ex">python</span> scripts/download_past_year.py <span class="at">--start-date</span> 2016-01-01 <span class="at">--end-date</span> 2026-01-01</span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Run full pipeline (SVD, text embeddings, fusion, similarity cache)</span></span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a><span class="ex">python</span> <span class="at">-m</span> pipeline.run_pipeline <span class="at">--db-path</span> data/motions.db <span class="dt">\</span></span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a> <span class="at">--start-date</span> 2016-01-01 <span class="at">--end-date</span> 2026-01-01 <span class="dt">\</span></span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a> <span class="at">--window-size</span> quarterly <span class="at">--text-batch-size</span> 200</span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Enrich with full motion body text</span></span>
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a><span class="ex">python</span> scripts/sync_motion_content.py <span class="at">--db-path</span> data/motions.db</span></code></pre></div><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></div>
<p>The DB grows to ~18 GB for the full dataset including body text. All computation — SVD, fusion, similarity — runs locally on a single machine.</p>
<p>Democracy is more legible than it looks.</p>
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thoughts/blog-post-political-compass.md
+29 -29
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@@ -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.
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---
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.
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}
};
// 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);
}
thoughts/blog-post-political-compass_files/libs/quarto-html/tabsets/tabsets.js
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// 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);
}
}
});
}
thoughts/blog-post-political-compass_files/libs/quarto-html/tippy.css
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.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 10px)}.tippy-box{position:relative;background-color:#333;color:#fff;border-radius:4px;font-size:14px;line-height:1.4;white-space:normal;outline:0;transition-property:transform,visibility,opacity}.tippy-box[data-placement^=top]>.tippy-arrow{bottom:0}.tippy-box[data-placement^=top]>.tippy-arrow:before{bottom:-7px;left:0;border-width:8px 8px 0;border-top-color:initial;transform-origin:center top}.tippy-box[data-placement^=bottom]>.tippy-arrow{top:0}.tippy-box[data-placement^=bottom]>.tippy-arrow:before{top:-7px;left:0;border-width:0 8px 8px;border-bottom-color:initial;transform-origin:center bottom}.tippy-box[data-placement^=left]>.tippy-arrow{right:0}.tippy-box[data-placement^=left]>.tippy-arrow:before{border-width:8px 0 8px 8px;border-left-color:initial;right:-7px;transform-origin:center left}.tippy-box[data-placement^=right]>.tippy-arrow{left:0}.tippy-box[data-placement^=right]>.tippy-arrow:before{left:-7px;border-width:8px 8px 8px 0;border-right-color:initial;transform-origin:center right}.tippy-box[data-inertia][data-state=visible]{transition-timing-function:cubic-bezier(.54,1.5,.38,1.11)}.tippy-arrow{width:16px;height:16px;color:#333}.tippy-arrow:before{content:"";position:absolute;border-color:transparent;border-style:solid}.tippy-content{position:relative;padding:5px 9px;z-index:1}
thoughts/blog-post-political-compass_files/libs/quarto-html/tippy.umd.min.js
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