- 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