---
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