Commit Graph
5 Commits
Author SHA1 Message Date
sgeboers 76b499cdc0 feat(analysis): Overton window breakpoint analysis with opposition control and SVD drift
Quantify 2024 breakpoint in centrist support (d=+0.68 overall, d=+0.85 opposition-only),
domain decomposition, extremity-stratified pass rates, and manual LLM audit (75% agreement).
SVD center drift aborted due to axis instability (9/10 consecutive window pairs fail stability threshold).
2026-05-08 23:14:34 +02:00
sgeboers 375955dbc4 cleanup: merge session ledgers into docs/solutions and delete artifacts
- 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
2026-04-30 23:24:43 +02:00
sgeboers 67eda93cb1 docs: add overtone shift analysis and insights
- Add deep dive analysis on SVD axis overtone shift (docs/research/)
- Update brainstorm with results documenting key finding: stability and
  overtone shift are independent phenomena
- Add learning doc about axis stability vs semantic drift independence
- Update drift report with detailed findings
2026-04-05 19:40:23 +02:00
sgeboers dafdfd5370 feat: add motion semantic drift analysis script
- 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.
2026-04-05 19:21:46 +02:00
sgeboers 50fafeecf3 feat: add motion semantic drift analysis script
- 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
2026-04-05 12:24:46 +02:00