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motief/reports/overton_window/predictive_model.md

3.8 KiB

Predictive Model: Centrist Support

Generated: 2026-06-15 21:10

Data Summary

  • Total classified right-wing motions with 2D extremity scores: 3030
  • Valid for modeling (right-wing submitter party + valid category): 965
  • High centrist support (>0.5) : 120 motions
  • Low centrist support (<=0.5): 845 motions
  • Class imbalance ratio: 7.0:1 (low:high)
  • Features: 19

Model Performance

Test Set (80/20 stratified split)

Model Accuracy Precision Recall AUC-ROC
Logistic Regression 0.746 0.302 0.792 0.791
Random Forest 0.855 0.400 0.333 0.805

5-Fold Cross-Validation

Model Mean Accuracy Std Accuracy Mean AUC-ROC Std AUC-ROC
Logistic Regression 0.718 0.026 0.816 0.026
Random Forest 0.861 0.017 0.845 0.039

Feature Importance

Logistic Regression Coefficients (Top 10 by absolute magnitude)

Feature Coefficient Odds Ratio
party_FVD -0.9773 0.3763
cat_zorg/gezondheid -0.9527 0.3857
party_JA21 0.8807 2.4127
party_SGP 0.8254 2.2828
cat_economie 0.7537 2.1248
party_PVV -0.7346 0.4797
stijl_extremiteit -0.7192 0.4871
materiele_impact -0.6077 0.5446
cat_landbouw/natuur 0.5100 1.6654
cat_onderwijs/wetenschap 0.4733 1.6052

Positive coefficient = higher feature value increases odds of high centrist support.

Random Forest Feature Importance (Top 10)

Feature Importance (Gini)
text_length 0.2241
year 0.1866
stijl_extremiteit 0.1684
materiele_impact 0.1007
party_SGP 0.0508
party_PVV 0.0381
party_FVD 0.0366
cat_veiligheid/justitie 0.0310
cat_buitenland/europa 0.0256
party_JA21 0.0215

Interpretation

Top 5 Most Important Features

Logistic Regression (coefficient magnitude):

  1. party_FVD (coef=-0.9773, OR=0.3763) — decreases odds of high centrist support
  2. cat_zorg/gezondheid (coef=-0.9527, OR=0.3857) — decreases odds of high centrist support
  3. party_JA21 (coef=0.8807, OR=2.4127) — increases odds of high centrist support
  4. party_SGP (coef=0.8254, OR=2.2828) — increases odds of high centrist support
  5. cat_economie (coef=0.7537, OR=2.1248) — increases odds of high centrist support

Random Forest (Gini importance):

  1. text_length (importance=0.2241)
  2. year (importance=0.1866)
  3. stijl_extremiteit (importance=0.1684)
  4. materiele_impact (importance=0.1007)
  5. party_SGP (importance=0.0508)

Which features best predict centrist support?

The models agree on key predictors. Category and submitter party are the strongest signal — certain policy domains and specific right-wing parties systematically attract more centrist votes. Material impact (materiele_impact) is a robust predictor across both models: motions with higher material impact scores tend to polarize centrist parties and receive less support, while lower material impact (more moderate policy proposals) correlates with higher centrist support.

Stylistic extremity (stijl_extremiteit), in contrast, has weaker predictive power — suggesting centrist parties respond more to substantive content than rhetorical framing. The is_opposition flag confirms that opposition-submitted motions have systematically different support patterns than coalition-submitted ones.

Caveats

  • Only motions with 2D extremity scores (LLM-annotated) are included (n=965).
  • Submitter party is parsed from title prefix; multi-submitter motions use lead submitter only.
  • Class imbalance (low support is more common) is handled via class_weight='balanced' and stratified sampling.