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

reports/drift/report.md

@@ -0,0 +1,33 @@
+# Motion Semantic Drift Analysis Report
+
+**Windows analyzed:** 2016, 2017, 2018, 2019, 2022, 2023, 2024, 2025, 2026
+**Top N motions per axis:** 20
+
+---
+
+## Summary
+
+- **Stable axes:** None
+- **Axes with inflection points:** 0
+- **Parties with cross-ideological voting:** 0
+
+## Axis Stability
+
+**Stable axes (similarity > 0.7):** None
+**Reordered axes:** [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+**Unstable axes:** []
+
+## Semantic Drift
+
+No drift data available (no stable axes or insufficient data).
+
+## Party Voting Analysis
+
+No cross-ideological voting detected.
+
+## Methodology
+
+- **Axis stability:** Jaccard similarity of top-N motion rankings per component across windows
+- **Semantic drift:** Cosine distance between fused embedding centroids of top-N motions per axis
+- **Inflection points:** Drift rate exceeding 2× median drift rate
+- **Cross-ideological voting:** Parties voting 'voor' on motions where canonical opposite-wing parties have high loadings