feat(overton): coalition coding fix + regenerate breakpoint analysis

docs/plans/2026-05-25-001-overton-window-analysis-gaps-plan.md

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+---
+title: "Address Critical Gaps in Overton Window Analysis"
+type: feat
+status: active
+date: 2026-05-25
+---
+
+# Address Critical Gaps in Overton Window Analysis
+
+## Summary
+
+The current Overton window synthesis identifies a structural break in centrist voting behavior post-2024 but leaves critical analytical gaps unresolved. This plan addresses the seven most important gaps: temporal trajectory analysis, 2D extremity decomposition, systematic mechanism classification, causal mechanism exploration, left-wing response patterns, motion success correlation, and quarterly granularity. The goal is to transform the current "what happened" analysis into a "how and why" explanation.
+
+## Problem Frame
+
+The synthesis report establishes that centrist support for right-wing motions surged from 0.251 to 0.507 (d=+0.65) and that right-wing parties moderated their proposals (material impact 2.78→2.43). However, the analysis relies on a binary pre/post-2024 split that obscures the actual dynamics. We don't know whether the shift was immediate (post-election shock) or gradual (learning curve), whether the 2D extremity trends diverge over time, whether the 24-motion mechanism sample generalizes, or what actually caused the behavioral change. These gaps prevent us from distinguishing between competing explanations: strategic adaptation by right-wing parties, genuine ideological convergence by centrists, coalition dynamics, or external shocks.
+
+## Requirements
+
+- R1. Replace binary pre/post-2024 analysis with continuous temporal trajectories showing when and how the shift occurred
+- R2. Decompose 2D extremity scores into separate stylistic and material trend lines to test whether the "flat single-dimension trend" masks diverging trajectories
+- R3. Systematically classify mechanisms across a representative sample (not just 24 top motions) to validate the consensus framing hypothesis
+- R4. Identify causal mechanisms by correlating the timing of the shift with political events (Schoof cabinet formation, European rightward shift, specific policy crises)
+- R5. Analyze left-wing voting patterns to determine whether the shift reflects right-wing moderation, centrist acceptance, or left-wing opposition hardening
+- R6. Correlate centrist support with actual motion passage to test whether high-support motions passed at higher rates
+- R7. Provide quarterly or monthly granularity to distinguish immediate post-election effects from gradual adaptation
+
+## Scope Boundaries
+
+- In scope: Quantitative analysis of existing data (motions, votes, 2D scores, SVD positions). No new data collection.
+- Out of scope: Qualitative interviews, media analysis, public opinion data, comparative analysis with other countries.
+- Deferred: Full causal inference modeling (diff-in-diff, regression discontinuity) — requires more sophisticated statistical framework than current descriptive approach.
+
+## Key Technical Decisions
+
+- **Temporal unit**: Use quarterly aggregation (Q1 2016 through Q2 2026 = 42 quarters). Monthly would be too noisy; annual loses the 2024 breakpoint resolution.
+- **2D extremity analysis**: Compute separate yearly means for stylistic and material scores, then test for divergence using paired t-tests or Wilcoxon signed-rank tests.
+- **Mechanism classification**: Use the existing 24-motion taxonomy (consensus framing, institutional, welfare, procedural, local, coalition, symbolic, targeted restriction, system dismantling, crisis response) and apply it to a stratified sample of 200 motions (50 pre-2024, 150 post-2024) using LLM classification with manual validation of 20%.
+- **Causal timing**: Identify the exact quarter when centrist support crossed the 0.4 threshold (midpoint between pre and post means) and correlate with political events.
+- **Left-wing analysis**: Compute left_support_mp (already exists) and analyze whether left-wing opposition hardened (decreased support) or remained stable.
+- **Success correlation**: Compute pass_rate for motions binned by centrist_support quartiles (0-0.25, 0.25-0.5, 0.5-0.75, 0.75-1.0) and test for monotonic relationship.
+
+## Implementation Units
+
+### U1. Temporal Trajectory Analysis
+
+**Goal:** Replace binary pre/post analysis with continuous quarterly trajectories showing the exact timing and shape of the centrist support shift.
+
+**Requirements:** R1, R7
+
+**Dependencies:** None
+
+**Files:**
+- Create: `analysis/right_wing/temporal_trajectory.py`
+- Output: `reports/overton_window/temporal_trajectory.md`
+- Output: `reports/overton_window/temporal_trajectory_figure.png`
+
+**Approach:**
+- Aggregate centrist_support_strict by quarter (2016-Q1 through 2026-Q2)
+- Compute rolling 3-quarter moving average to smooth noise
+- Identify the inflection point: first quarter where centrist_support > 0.4
+- Plot trajectory with confidence intervals (bootstrap resampling, 1000 iterations)
+- Annotate political events: 2021 election, 2023 election, July 2024 Schoof cabinet formation
+- Compute quarterly motion counts to show volume surge timing
+
+**Patterns to follow:**
+- `analysis/right_wing/temporal_analysis.py` — yearly aggregation pattern
+- `analysis/right_wing/overton_breakpoint_analysis.py` — matplotlib chart patterns
+
+**Test scenarios:**
+- Happy path: Script produces quarterly aggregates for all 42 quarters, identifies inflection point, generates figure with 5 lines (overall, opposition-only, migration, non-migration, all-motions baseline)
+- Edge case: Quarters with <10 motions should show wider confidence intervals
+- Edge case: 2026-Q2 (partial year) should be flagged as incomplete
+
+**Verification:**
+- `temporal_trajectory.md` contains a table with quarterly centrist_support, motion counts, and confidence intervals
+- Figure shows the exact quarter when the shift began and whether it was immediate or gradual
+- Inflection point is explicitly identified and correlated with political events
+
+### U2. 2D Extremity Temporal Decomposition
+
+**Goal:** Test whether the "flat single-dimension trend" masks diverging trajectories when stylistic and material scores are analyzed separately.
+
+**Requirements:** R2
+
+**Dependencies:** U1 (uses same temporal framework)
+
+**Files:**
+- Create: `analysis/right_wing/extremity_2d_temporal.py`
+- Output: `reports/overton_window/extremity_2d_temporal.md`
+- Output: `reports/overton_window/extremity_2d_temporal_figure.png`
+
+**Approach:**
+- Join extremity_scores_2d with right_wing_motions to get year for each motion
+- Compute yearly means for stylistic_score and material_score separately
+- Plot both trajectories on the same figure with the original single-dimension score for comparison
+- Test for divergence: paired Wilcoxon signed-rank test on yearly (stylistic, material) pairs
+- Compute the gap (material - stylistic) over time to see if it's widening, narrowing, or stable
+- Stratify by domain (migration vs non-migration) to test whether the gap differs by policy area
+
+**Patterns to follow:**
+- `analysis/right_wing/extremity_rescore_2d.py` — 2D score structure
+- `analysis/right_wing/temporal_analysis.py` — yearly aggregation
+
+**Test scenarios:**
+- Happy path: Script produces yearly means for both dimensions, generates figure with 3 lines (stylistic, material, original), computes divergence test statistic
+- Edge case: Years with <50 scored motions should be flagged as low-confidence
+- Integration: Results should be consistent with the aggregate findings (material > stylistic, r≈0.47)
+
+**Verification:**
+- `extremity_2d_temporal.md` contains a table with yearly stylistic and material means
+- Figure shows whether the two dimensions diverged over time or moved in parallel
+- Divergence test result is reported (p-value or effect size)
+
+### U3. Systematic Mechanism Classification
+
+**Goal:** Validate the consensus framing hypothesis by classifying mechanisms across a representative sample of 200 motions, not just the 24 highest-support motions.
+
+**Requirements:** R3
+
+**Dependencies:** None
+
+**Files:**
+- Create: `analysis/right_wing/mechanism_classification.py`
+- Output: `reports/overton_window/mechanism_classification.md`
+
+**Approach:**
+- Stratified sampling: 50 pre-2024 motions (25 high centrist support, 25 low), 150 post-2024 motions (75 high, 75 low)
+- Use LLM classification with the 10-mechanism taxonomy from the synthesis report
+- Prompt template: "Classify this motion's primary mechanism for gaining centrist support: [taxonomy with definitions]"
+- Manual validation: randomly sample 40 motions (20%) and have a human reviewer confirm or correct the classification
+- Compute mechanism distribution by period (pre vs post) and by centrist support level (high vs low)
+- Test whether consensus framing is more common in high-support post-2024 motions than in other groups
+
+**Patterns to follow:**
+- `analysis/right_wing/derive_categories.py` — LLM classification pattern
+- `analysis/right_wing/extremity_rescore_2d.py` — batch processing with validation
+
+**Test scenarios:**
+- Happy path: Script classifies 200 motions, produces mechanism distribution table, computes chi-squared test for mechanism × period × support interaction
+- Edge case: LLM returns invalid mechanism labels should be caught and re-prompted
+- Integration: Manual validation should achieve >80% agreement with LLM classifications
+
+**Verification:**
+- `mechanism_classification.md` contains a table showing mechanism distribution across 4 groups (pre-high, pre-low, post-high, post-low)
+- Chi-squared test result is reported
+- Manual validation agreement rate is reported
+
+### U4. Causal Timing Analysis
+
+**Goal:** Identify the exact timing of the centrist support shift and correlate it with political events to distinguish between competing causal explanations.
+
+**Requirements:** R4, R7
+
+**Dependencies:** U1 (uses quarterly trajectory data)
+
+**Files:**
+- Create: `analysis/right_wing/causal_timing.py`
+- Output: `reports/overton_window/causal_timing.md`
+
+**Approach:**
+- Use the quarterly trajectory from U1
+- Identify the inflection point: first quarter where centrist_support > 0.4 (midpoint between pre=0.25 and post=0.51)
+- Compute the "shift velocity": change in centrist_support per quarter in the 4 quarters before and after the inflection point
+- Correlate with political events timeline:
+  - March 2021: Rutte IV election
+  - November 2023: Schoof election (PVV victory)
+  - July 2024: Schoof cabinet formation
+  - Ongoing: European rightward shift (Meloni 2022, Sweden 2022, Finland 2023)
+- Test whether the shift was immediate (single-quarter jump) or gradual (multi-quarter ramp)
+- Compute "event proximity": did the shift begin before or after the Schoof cabinet formation?
+
+**Patterns to follow:**
+- `analysis/right_wing/overton_breakpoint_analysis.py` — breakpoint detection logic
+
+**Test scenarios:**
+- Happy path: Script identifies inflection point quarter, computes shift velocity, generates timeline figure with annotated events
+- Edge case: If no clear inflection point (gradual shift), report the quarter with the steepest slope
+- Integration: Results should be consistent with U1 trajectory analysis
+
+**Verification:**
+- `causal_timing.md` explicitly states which quarter the shift began
+- Shift velocity is reported (quarters to reach 80% of the total shift)
+- Timeline figure shows the relationship between the shift and political events
+
+### U5. Left-Wing Response Analysis
+
+**Goal:** Determine whether the centrist support surge reflects right-wing moderation, centrist acceptance, or left-wing opposition hardening.
+
+**Requirements:** R5
+
+**Dependencies:** None
+
+**Files:**
+- Create: `analysis/right_wing/left_wing_response.py`
+- Output: `reports/overton_window/left_wing_response.md`
+- Output: `reports/overton_window/left_wing_response_figure.png`
+
+**Approach:**
+- Compute left_support_mp (already exists in right_wing_motions) for pre and post-2024
+- Stratify by left party: SP, PvdA, GroenLinks, PvdD, Volt, DENK
+- Test whether left-wing opposition hardened (decreased support) or remained stable
+- Compute the "polarization gap": (centrist_support - left_support) over time
+- If the gap widened, it could reflect centrist acceptance OR left-wing hardening OR both
+- Stratify by domain to see if left-wing hardening is concentrated in migration (where centrist acceptance is highest)
+
+**Patterns to follow:**
+- `analysis/right_wing/overton_breakpoint_analysis.py` — party-level vote analysis
+- `analysis/right_wing/migrate_mp_level_metrics.py` — left_support_mp computation
+
+**Test scenarios:**
+- Happy path: Script computes pre/post left_support_mp by party, generates figure showing left-wing trajectory vs centrist trajectory
+- Edge case: Parties with <5 MPs in a given year should be excluded from party-level analysis
+- Integration: Results should be consistent with the synthesis report's claim that "left opposition hardened"
+
+**Verification:**
+- `left_wing_response.md` contains a table with pre/post left_support_mp by party
+- Figure shows whether left-wing opposition hardened, softened, or remained stable
+- Polarization gap trajectory is reported
+
+### U6. Motion Success Correlation
+
+**Goal:** Test whether motions with high centrist support actually passed at higher rates, validating that centrist support translates to legislative success.
+
+**Requirements:** R6
+
+**Dependencies:** None
+
+**Files:**
+- Create: `analysis/right_wing/success_correlation.py`
+- Output: `reports/overton_window/success_correlation.md`
+
+**Approach:**
+- Compute pass_rate for right-wing motions binned by centrist_support quartiles: [0-0.25], (0.25-0.5], (0.5-0.75], (0.75-1.0]
+- Test for monotonic relationship using Cochran-Armitage trend test
+- Stratify by period (pre vs post-2024) to see if the relationship strengthened after the shift
+- Control for motion type: government motions (from coalition parties) vs opposition motions
+- Compute "success premium": pass_rate(high support) - pass_rate(low support)
+
+**Patterns to follow:**
+- `analysis/right_wing/overton_breakpoint_analysis.py` — pass rate computation (even though it's 96%+, we're testing for variation within that 4%)
+
+**Test scenarios:**
+- Happy path: Script computes pass_rate by centrist_support quartile, performs trend test, generates table
+- Edge case: Quartiles with <50 motions should be flagged as low-confidence
+- Integration: Results should show whether the 96%+ pass rate is uniform or varies by centrist support level
+
+**Verification:**
+- `success_correlation.md` contains a table with pass_rate by centrist_support quartile
+- Trend test result is reported (p-value)
+- Success premium is computed and interpreted
+
+### U7. Synthesis Update
+
+**Goal:** Integrate all new findings into the synthesis report, updating the verdict and uncertainty hierarchy.
+
+**Requirements:** R1-R7
+
+**Dependencies:** U1, U2, U3, U4, U5, U6
+
+**Files:**
+- Modify: `reports/overton_window/overton_window_synthesis.md`
+
+**Approach:**
+- Update the "Three Indicators at a Glance" table with new temporal and 2D findings
+- Add a new section "Temporal Dynamics" summarizing U1 and U4 findings (when the shift happened, how fast)
+- Add a new section "2D Extremity Trajectories" summarizing U2 findings (whether stylistic and material diverged)
+- Update the "Mechanisms of Influence" section with U3 systematic classification results
+- Add a new section "Causal Mechanisms" summarizing U4 timing analysis and event correlation
+- Add a new section "Left-Wing Response" summarizing U5 findings
+- Update the "Uncertainty Hierarchy" table to reflect which gaps are now resolved
+- Revise the verdict if new evidence changes the interpretation
+
+**Patterns to follow:**
+- Existing synthesis report structure
+
+**Test scenarios:**
+- Happy path: All U1-U6 outputs are integrated, uncertainty hierarchy is updated, verdict is revised if needed
+- Integration: Report remains internally consistent after updates
+
+**Verification:**
+- Synthesis report contains new sections for temporal dynamics, 2D trajectories, causal mechanisms, and left-wing response
+- Uncertainty hierarchy table reflects the current state of knowledge
+- Verdict is supported by all available evidence
+
+## System-Wide Impact
+
+- **No database changes:** All analysis uses existing tables (right_wing_motions, extremity_scores_2d, mp_votes, motions)
+- **No UI changes:** All outputs are markdown reports and PNG figures
+- **No agent_tools changes:** Analysis scripts are standalone
+- **Reproducibility:** All scripts are deterministic given the same database state
+
+## Risks & Dependencies
+
+| Risk | Mitigation |
+|------|------------|
+| Quarterly aggregation produces noisy estimates for low-volume quarters | Use 3-quarter moving average and bootstrap confidence intervals |
+| LLM mechanism classification may be inconsistent | Manual validation of 20% sample, re-prompt invalid classifications |
+| Causal timing analysis may be ambiguous (gradual vs immediate shift) | Report both the inflection point and the shift velocity; let the data speak |
+| Left-wing analysis may be underpowered for small parties | Exclude parties with <5 MPs in a given year from party-level analysis |
+| Pass rate analysis may find no variation (96%+ ceiling) | Report the result honestly; if no correlation exists, say so |
+
+## Sources & References
+
+- **Current synthesis:** `reports/overton_window/overton_window_synthesis.md`
+- **2D extremity data:** `extremity_scores_2d` table (2,869 motions scored)
+- **Temporal framework:** `analysis/right_wing/temporal_analysis.py`
+- **Mechanism taxonomy:** Synthesis report Section "Mechanisms of Influence"
+- **Left-wing data:** `left_support_mp` column in `right_wing_motions` table

docs/plans/2026-05-26-001-overton-improvements-extensions-plan.md

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+---
+title: "Overton Window Analysis: Improvements and Extensions"
+type: feat
+status: active
+date: 2026-05-26
+origin: docs/plans/2026-05-25-001-overton-window-analysis-gaps-plan.md
+---
+
+# Overton Window Analysis: Improvements and Extensions
+
+## Summary
+
+The current Overton window analysis is methodologically strong — multi-indicator, 2D extremity decomposition, causal timing, mechanism classification. But it has structural gaps that limit interpretability. This plan addresses six gaps: (1) right-wing party differentiation (PVV vs FVD vs JA21 vs SGP — who filed the motions?), (2) coalition coding fix (split 2024 into Rutte IV / Schoof periods), (3) voting margin analysis (the 96% ceiling makes pass rate useless — use actual voor/tegen percentages instead), (4) SVD temporal trajectory (plot the spatial drift over 10 annual windows), (5) mechanism classification validation (second classifier for inter-rater reliability), and (6) predictive modeling (what motion features predict centrist support?). Each unit is independent and can be executed in parallel.
+
+## Problem Frame
+
+The synthesis report establishes that the Overton window did not shift right — right-wing parties moderated toward it. But the analysis treats right-wing parties as a bloc, uses a binary coalition coding that misattributes early 2024 motions, relies on pass rate as a success metric despite its 96% ceiling, and has no SVD visualization of the spatial drift. The mechanism classification (200 motions, single classifier) lacks inter-rater validation. Most critically, we have no predictive model: we can describe *what* happened but not *what features* predict which motions will gain centrist support.
+
+## Requirements
+
+- R1. Break down centrist support, extremity, and mechanism patterns by right-wing party (PVV, FVD, JA21, SGP) to identify which party drives the moderation effect
+- R2. Fix coalition coding by splitting 2024 into pre-Schoof (Rutte IV, Jan-Jun) and post-Schoof (Schoof, Jul-Dec) periods
+- R3. Replace pass rate with voting margin analysis (actual voor/tegen percentages) as the primary success metric
+- R4. Visualize SVD spatial drift over 10 annual windows showing centrist and right-wing trajectories
+- R5. Validate mechanism classification with a second classifier and compute inter-rater reliability (Cohen's kappa)
+- R6. Build a predictive model for centrist support using motion features (category, extremity scores, submitter party, mechanism, text features)
+
+## Scope Boundaries
+
+- In scope: Quantitative analysis of existing data, new visualizations, predictive modeling
+- Out of scope: Qualitative interviews, media analysis, public opinion data, international comparison
+- Deferred: Cross-domain interaction analysis (migration × security), network/gateway motion analysis, submitter-level MP analysis
+
+## Key Technical Decisions
+
+- **Party differentiation:** Use `voting_results` JSON from motions table to extract per-party vote counts. Compute party-specific centrist support separately for PVV, FVD, JA21, SGP motions.
+- **Coalition coding:** Split 2024 at July 1, 2024 (Schoof cabinet formation). Motions dated before July 2024 use Rutte IV coalition; after use Schoof coalition.
+- **Voting margin:** Compute `margin = (voor - tegen) / (voor + tegen + afwezig)` per motion. This gives a continuous [-1, 1] scale instead of binary pass/fail.
+- **SVD trajectory:** Use existing `load_party_scores_all_windows_aligned()` to get 2D positions for all parties across 10 windows. Plot as trajectory arrows.
+- **Mechanism validation:** Use a second LLM (different model or different prompt) to classify the same 200 motions. Compute Cohen's kappa.
+- **Predictive model:** Use logistic regression or random forest with features: category, stijl_extremiteit, materiele_impact, submitter_party, mechanism, text_length, keyword_count.
+
+## Implementation Units
+
+### U1. Right-Wing Party Differentiation
+
+**Goal:** Break down all key metrics by right-wing party to identify which party drives the moderation effect.
+
+**Requirements:** R1
+
+**Dependencies:** None
+
+**Files:**
+- Create: `analysis/right_wing/party_differentiation.py`
+- Output: `reports/overton_window/party_differentiation.md`
+- Output: `reports/overton_window/party_differentiation_figure.png`
+
+**Approach:**
+- Parse `voting_results` JSON from motions table to identify the submitter party for each right-wing motion
+- Compute per-party: motion volume, mean centrist_support, mean extremity (2D), mechanism distribution
+- Stratify by period (pre vs post-2024)
+- Test whether PVV's moderation is distinct from FVD/JA21/SGP
+- Plot: 4-panel figure with (a) volume over time, (b) centrist support over time, (c) extremity over time, (d) mechanism distribution
+
+**Patterns to follow:**
+- `analysis/right_wing/overton_breakpoint_analysis.py` — party-level analysis patterns
+- `analysis/right_wing/classify_motions.py` — submitter parsing from title
+
+**Test scenarios:**
+- Happy path: Script produces per-party metrics for PVV, FVD, JA21, SGP across all years
+- Edge case: Multi-submitter motions (use first submitter)
+- Edge case: Parties with <10 motions in a year → exclude from party-level analysis
+
+**Verification:**
+- Report contains per-party tables for volume, centrist support, extremity, mechanisms
+- Figure shows whether moderation is PVV-specific or party-general
+
+### U2. Coalition Coding Fix
+
+**Goal:** Split 2024 into pre-Schoof (Rutte IV) and post-Schoof (Schoof) periods to eliminate coalition coding ambiguity.
+
+**Requirements:** R2
+
+**Dependencies:** None
+
+**Files:**
+- Modify: `analysis/right_wing/overton_breakpoint_analysis.py` (coalition coding logic)
+- Modify: `analysis/right_wing/temporal_trajectory.py` (quarterly analysis)
+- Output: Updated reports with corrected coalition coding
+
+**Approach:**
+- Define coalition periods: Rutte IV (2022-Oct to 2024-Jul), Schoof (2024-Jul to present)
+- Update `is_opposition` logic to use motion date for period detection
+- Re-run opposition-only analysis with corrected coding
+- Compare results with original binary coding
+
+**Patterns to follow:**
+- `analysis/right_wing/overton_breakpoint_analysis.py` — existing coalition coding at line ~200
+
+**Test scenarios:**
+- Happy path: Opposition-only analysis shows corrected centrist support trajectory
+- Edge case: Motions in July 2024 (transition month) → assign to Schoof
+- Integration: Results should be consistent with temporal trajectory findings
+
+**Verification:**
+- Opposition-only centrist support trajectory is recalculated with corrected coding
+- Report explicitly states the coding change and its impact on findings
+
+### U3. Voting Margin Analysis
+
+**Goal:** Replace binary pass/fail with continuous voting margin as the primary success metric.
+
+**Requirements:** R3
+
+**Dependencies:** None
+
+**Files:**
+- Create: `analysis/right_wing/voting_margin.py`
+- Output: `reports/overton_window/voting_margin.md`
+- Output: `reports/overton_window/voting_margin_figure.png`
+
+**Approach:**
+- Compute `margin = (voor - tegen) / (voor + tegen + afwezig)` for each right-wing motion
+- Analyze margin distribution by centrist support quartile
+- Test whether higher centrist support → higher margin (not just pass/fail)
+- Stratify by period (pre vs post-2024)
+- Plot: margin distribution by centrist support quartile, with period comparison
+
+**Patterns to follow:**
+- `analysis/right_wing/success_correlation.py` — existing pass rate analysis
+
+**Test scenarios:**
+- Happy path: Script computes margins for all right-wing motions, produces distribution figure
+- Edge case: Motions with 0 votes → exclude
+- Edge case: Motions with unanimous support → margin = 1.0
+
+**Verification:**
+- Report contains margin distribution by centrist support quartile
+- Figure shows whether centrist support predicts voting margin (continuous) better than pass rate (binary)
+
+### U4. SVD Temporal Trajectory Visualization
+
+**Goal:** Visualize SVD spatial drift over 10 annual windows showing centrist and right-wing party trajectories.
+
+**Requirements:** R4
+
+**Dependencies:** None
+
+**Files:**
+- Create: `analysis/right_wing/svd_trajectory_viz.py`
+- Output: `reports/overton_window/svd_trajectory_figure.png`
+
+**Approach:**
+- Use `load_party_scores_all_windows_aligned()` to get 2D positions for all parties across 10 windows
+- Plot: 2D compass with trajectory arrows for centrist parties (VVD, D66, CDA, NSC, BBB, CU) and right-wing parties (PVV, FVD, JA21, SGP)
+- Color by party, arrow direction shows temporal progression
+- Annotate windows with year labels
+
+**Patterns to follow:**
+- `analysis/right_wing/overton_svd_drift.py` — existing SVD drift analysis
+- `explorer.py` — compass plotting with PARTY_COLOURS
+
+**Test scenarios:**
+- Happy path: Figure shows clear trajectory arrows for all parties
+- Edge case: Missing party in a window → skip that arrow segment
+
+**Verification:**
+- Figure shows whether centrist parties moved left while right-wing parties moved right
+- Trajectory arrows are clearly labeled with year markers
+
+### U5. Mechanism Classification Validation
+
+**Goal:** Validate mechanism classification with a second classifier and compute inter-rater reliability.
+
+**Requirements:** R5
+
+**Dependencies:** None
+
+**Files:**
+- Create: `analysis/right_wing/mechanism_validation.py`
+- Output: `reports/overton_window/mechanism_validation.md`
+
+**Approach:**
+- Use a second LLM (different model or different prompt) to classify the same 200 motions
+- Compute Cohen's kappa for inter-rater reliability
+- Report disagreements and resolve them
+- Update mechanism classification with validated results
+
+**Patterns to follow:**
+- `analysis/right_wing/mechanism_classification.py` — existing classification
+
+**Test scenarios:**
+- Happy path: Second classifier produces classifications for all 200 motions
+- Edge case: Disagreements → report and resolve
+
+**Verification:**
+- Report contains Cohen's kappa score
+- Disagreements are documented and resolved
+
+### U6. Predictive Modeling
+
+**Goal:** Build a predictive model for centrist support using motion features.
+
+**Requirements:** R6
+
+**Dependencies:** U1 (party differentiation), U3 (voting margin)
+
+**Files:**
+- Create: `analysis/right_wing/predictive_model.py`
+- Output: `reports/overton_window/predictive_model.md`
+- Output: `reports/overton_window/predictive_model_figure.png`
+
+**Approach:**
+- Features: category, stijl_extremiteit, materiele_impact, submitter_party, mechanism, text_length, keyword_count
+- Target: centrist_support (binary: >0.5 = high, <=0.5 = low)
+- Models: logistic regression (interpretable), random forest (accuracy)
+- Evaluate: accuracy, precision, recall, AUC-ROC
+- Feature importance: which features best predict centrist support?
+
+**Patterns to follow:**
+- `analysis/right_wing/extremity_rescore_2d.py` — batch processing patterns
+
+**Test scenarios:**
+- Happy path: Model achieves AUC-ROC > 0.7
+- Edge case: Missing features → impute with median/mode
+
+**Verification:**
+- Report contains model performance metrics and feature importance
+- Figure shows ROC curve and feature importance plot
+
+## System-Wide Impact
+
+- **No database changes:** All analysis uses existing tables
+- **No UI changes:** All outputs are markdown reports and PNG figures
+- **No agent_tools changes:** Analysis scripts are standalone
+- **Reproducibility:** All scripts are deterministic given the same database state
+
+## Risks & Dependencies
+
+| Risk | Mitigation |
+|------|------------|
+| Party differentiation may show PVV dominates everything | Report per-party sample sizes; exclude parties with <10 motions |
+| Coalition coding fix may not change findings | Report both codings and compare |
+| Voting margin may be correlated with pass rate | Compute correlation; if r>0.95, margin adds no value |
+| SVD trajectory may be too cluttered | Use separate panels for centrist and right-wing |
+| Mechanism validation may show low agreement | Report kappa; if <0.6, revise taxonomy |
+| Predictive model may overfit | Use cross-validation; report train/test split |
+
+## Sources & References
+
+- **Current synthesis:** `reports/overton_window/overton_window_synthesis.md`
+- **Temporal trajectory:** `reports/overton_window/temporal_trajectory.md`
+- **Mechanism classification:** `reports/overton_window/mechanism_classification.md`
+- **SVD drift:** `analysis/right_wing/overton_svd_drift.py`
+- **Party positions:** `analysis/explorer_data.py` — `load_party_scores_all_windows_aligned()`