- Fetched 276 new motions from Tweede Kamer API (2026-04-23 to 2026-05-31)
- Fixed classify_motions.py: DROP TABLE → CREATE TABLE IF NOT EXISTS
- Restored derived columns (centrist_support_strict, category, etc.) via migration
- Scored 180 missing motions in extremity_scores_2d (now 3,049 total, 0 missing)
- Re-ran temporal trajectory with updated data (inflection: 2024-Q2)
U1: JA21 drives moderation effect (+0.203 CS shift, only party with volume+support gains)
U2: Coalition coding split at July 2024 — opposition effect confirmed (d=0.85 vs 0.87)
U3: Voting margin (ρ=0.812 with centrist support) is far superior to pass rate
U4: SVD trajectory confirms spatial divergence — centrists moved left (Δx=-0.30), right stationary
U5: Mechanism classification Cohen's κ=0.41 (moderate) — taxonomy needs revision
U6: Predictive model AUC-ROC=0.81 — submitter party and category are strongest predictors
Material impact declined post-2024 (2.78→2.43, M>=4 share 23.7%→11.3%).
Right-wing strategic moderation: more motions, milder content, better
framing. The Overton window did not expand — right-wing proposals
shifted into the existing window. 'Acceptance through moderation'
replaces 'acceptance without conversion.'
SVD axes capture agreement structure — centrists 'moving left' means
voting patterns diverged from right-wing, not that parties changed
ideology. 'Acceptance without conversion' is a behavioral claim.
Documented as best-practice learning.
- Classified 24 post-2024 right-wing motions with CS>=0.5
- Dominant mechanisms: consensus framing (33%), institutional (21%), welfare (17%)
- Only 1 targeted restriction, zero system dismantling
- Right-wing gains centrist support through repackaging, not conversion
- Confirms acceptance-without-conversion dynamic at the mechanism level
- Pearson r=0.45 between stylistic and material impact (separable)
- Material impact averages 0.85 points above stylistic
- 36.8% of motions mask high-impact policy behind restrained language
- Original single-score conflates language vs substance
- Mark U4 mechanism analysis as in progress
- Project-local skill .opencode/skills/score-extremity/ for subagent dispatch
- Orchestrator extremity_rescore_2d.py with load_skill/sample/format/validate/store
- 16 TDD tests covering all orchestrator functions
- 117 motions scored by deepseek v4 flash subagents (12 parallel batches)
- Pearson r=0.45 between stylistic and material dimensions — separable
- Key finding: 36.8% of motions use restrained language for consequential policies
- 2d_extremity_correlation_report.md documents distribution, divergence patterns,
and implications for the Overton acceptance-without-conversion narrative
- Reclassified centrist to {D66, CDA, CU, NSC} — removing VVD/BBB
which are center-right coalition partners
- Added centrist_support_strict (0.251→0.507, d=+0.65), center_right_support,
and left_support_mp columns via migration script
- Figure 1 now shows center-right (VVD/BBB) support as orange dashed line
- New Figure 3: bar chart of left-party support for right-wing motions
(0.268→0.202, left opposition hardened)
- New report Section 6 covering left-wing support trends
- All analysis now uses strict centrist definition throughout
- Remove stale thoughts/ledgers/ and thoughts/shared/ artifacts
- Fix .gitignore duplicate .worktrees entry
- Move pyright to [dependency-groups] dev
- Replace hardcoded blog correlation with reproducible metric reference
- Add docs: verify-session-artifacts, fusion-vector-dimensions,
working-tree-hygiene
- Update blog-numbers-from-pipeline-outputs with correlation example
- Implement SVD axis stability using Lasso regression on fused embeddings
- Add overtone shift analysis to detect semantic content changes
- Implement semantic drift tracking for motion content over time
- Add party voting analysis with cross-ideological voting patterns
- Generate markdown report with visualizations
- Add comprehensive test suite with 12 passing tests
See reports/drift/report.md for analysis results.
- Add scripts/motion_drift.py: analyzes SVD axis stability, semantic drift,
and cross-ideological voting patterns across annual windows
- Add analysis/motion_drift.py: core analysis functions with Procrustes
alignment fallback using party-based sign consistency
- Add matplotlib dependency for static chart generation
- Add tests/test_motion_drift.py: 12 tests covering all analysis functions
- Report output: markdown with embedded PNG charts
Key findings from real data:
- No axes are fully stable (>0.7) across 2019-2026
- All axes show moderate consistency (0.40-0.47) — stable within periods
but flip between cabinet periods (2019/2022/2026 vs 2023/2024/2025)
- Party voting analysis detects cross-ideological voting patterns
Sven Geboers