parent
7ff3fec992
commit
2d5b28fe1b
@ -0,0 +1,309 @@ |
|||||||
|
--- |
||||||
|
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 |
||||||
@ -0,0 +1,255 @@ |
|||||||
|
--- |
||||||
|
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()` |
||||||
|
Before Width: | Height: | Size: 170 KiB After Width: | Height: | Size: 169 KiB |
|
Before Width: | Height: | Size: 153 KiB After Width: | Height: | Size: 153 KiB |
Loading…
Reference in new issue