Update model card with generalization findings

README.md

@@ -76,8 +76,39 @@ The model is steerable - changing attitudes while holding demographics constant
 
 | Attitude Config | Predicted Ideology |
 |-----------------|-------------------|
-| Satisfied + Economy better + More immigration | 5 (center) |
-| Dissatisfied + Economy worse + Fewer immigration | 7 (center-right) |
+| Satisfied + Economy better + More immigration | 2 (left) |
+| Dissatisfied + Economy worse + Fewer immigration | 6 (center-right) |
+
+**4-point ideology swing** from attitude changes alone, holding demographics constant.
+
+## Generalization to Unseen Questions
+
+We tested the model on CES questions it was **never trained on**:
+
+| Question Type | Example | Correlation (r) |
+|--------------|---------|-----------------|
+| **High-salience (Identity)** | COVID satisfaction | **0.60** |
+| **High-salience (Identity)** | Carbon tax position | **0.49** |
+| Low-salience (Policy) | Defence spending | 0.12 |
+| Low-salience (Policy) | Environment spending | -0.12 |
+
+### Key Finding
+
+The model learned **political identity**, not policy platforms:
+- **Carbon Tax** (r=0.49) vs **Environment Spending** (r=-0.12) — both are "about the environment" but carbon tax is a tribal identity marker while spending is a technocratic detail
+- The 3 psychographic variables compress the "culture war" aspects of Canadian politics
+- Model excels at identity/affect prediction, struggles with budget details
+
+### Implications
+
+This model is ideal for:
+- Simulating political discourse and polarization
+- Agent-based models of partisan sorting
+- Studying affective political identity
+
+Not suitable for:
+- Predicting specific policy preferences
+- Budget allocation modeling
 
 ## Citation