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motief/reports/overton_window/predictive_model.md

3.8 KiB

Predictive Model: Centrist Support

Generated: 2026-05-31 19:36

Data Summary

  • Total classified right-wing motions with 2D extremity scores: 2850
  • Valid for modeling (right-wing submitter party + valid category): 914
  • High centrist support (>0.5) : 115 motions
  • Low centrist support (<=0.5): 799 motions
  • Class imbalance ratio: 6.9:1 (low:high)
  • Features: 22

Model Performance

Test Set (80/20 stratified split)

Model Accuracy Precision Recall AUC-ROC
Logistic Regression 0.710 0.258 0.696 0.810
Random Forest 0.852 0.423 0.478 0.795

5-Fold Cross-Validation

Model Mean Accuracy Std Accuracy Mean AUC-ROC Std AUC-ROC
Logistic Regression 0.718 0.032 0.815 0.036
Random Forest 0.862 0.016 0.835 0.048

Feature Importance

Logistic Regression Coefficients (Top 10 by absolute magnitude)

Feature Coefficient Odds Ratio
cat_corona/pandemie -1.4680 0.2304
party_FVD -1.3282 0.2650
party_SGP 0.9877 2.6852
party_JA21 0.9264 2.5255
stijl_extremiteit -0.6859 0.5036
party_PVV -0.6394 0.5276
cat_onderwijs/cultuur 0.5472 1.7285
cat_zorg/gezondheid -0.4857 0.6153
materiele_impact -0.4741 0.6225
cat_overig 0.4658 1.5933

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

Random Forest Feature Importance (Top 10)

Feature Importance (Gini)
text_length 0.2137
year 0.1915
stijl_extremiteit 0.1410
materiele_impact 0.0946
party_SGP 0.0652
party_FVD 0.0489
party_PVV 0.0407
cat_veiligheid/justitie 0.0258
cat_defensie/buitenland 0.0246
party_JA21 0.0234

Interpretation

Top 5 Most Important Features

Logistic Regression (coefficient magnitude):

  1. cat_corona/pandemie (coef=-1.4680, OR=0.2304) — decreases odds of high centrist support
  2. party_FVD (coef=-1.3282, OR=0.2650) — decreases odds of high centrist support
  3. party_SGP (coef=0.9877, OR=2.6852) — increases odds of high centrist support
  4. party_JA21 (coef=0.9264, OR=2.5255) — increases odds of high centrist support
  5. stijl_extremiteit (coef=-0.6859, OR=0.5036) — decreases odds of high centrist support

Random Forest (Gini importance):

  1. text_length (importance=0.2137)
  2. year (importance=0.1915)
  3. stijl_extremiteit (importance=0.1410)
  4. materiele_impact (importance=0.0946)
  5. party_SGP (importance=0.0652)

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=914).
  • 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.