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title	date	category	module	problem_type	component	severity	applies_when	tags
SVD Labels Should Reflect Voting Patterns, Not Semantic Content	2026-04-04	docs/solutions/best-practices	stemwijzer	best_practice	brief_system	medium	[Labeling SVD (Singular Value Decomposition) components in voting analysis Interpreting PCA/SVD dimensions in political party voting data Creating axis labels for voting compass or similar applications]	[svd voting-analysis axis-labels dimensionality-reduction party-voting-patterns]

SVD Labels Should Reflect Voting Patterns, Not Semantic Content

Context

When labeling SVD components in the Stemwijzer explorer (explorer.py), initial labels were based on semantic analysis of motion titles — what topics motions appeared to discuss. However, SVD captures voting patterns, not semantic content.

This mismatch led to:

Labels that didn't match how parties actually voted
Right-wing parties appearing on the LEFT side of axes (violating the right-wing parties → right side constraint)
Confusion about what each component actually represents

Guidance

The Core Principle

SVD components represent voting unity patterns, not topic clusters.

When a motion appears on a component with a positive loading, it means parties that vote positively on that motion tend to vote similarly. The component captures this behavioral pattern, not the topic's semantic meaning.

Example: Component 1

Approach	Label	Why It's Wrong
Semantic	"Sociale zekerheid vs economische liberalisering"	Assumes defense + social care = welfare state
Voting	"Rechts kabinetsbeleid vs links oppositiebeleid"	Matches actual coalition vs opposition voting

Why Component 1 captures coalition-opposition:

9 coalition + center parties vote one way
6 opposition parties vote the other way
Motion topics can include defense (right votes for) AND social care (left votes for) because they're on opposite sides of the coalition-opposition divide

How to Label SVD Components

Analyze actual voting patterns first
- Query which parties vote positively vs negatively on each component
- Look for coalition/opposition splits, cross-block alliances, or isolated parties
Verify right-wing parties on RIGHT side
- Check PVV, FVD, JA21, SGP positions
- If they vote negatively while left parties vote positively, flip the axis
Don't assume semantics match voting
- A "defense" component may include social care motions if right-wing parties vote the same way on both
- Cross-block alliances (e.g., PVV with SP on welfare) create components that don't fit left-right semantics
Test with sample motions
- Top positive-loading motions should align with positive-voting parties' priorities
- Top negative-loading motions should align with negative-voting parties' priorities

Why This Matters

Without understanding that SVD captures voting patterns:

Labels will be misleading to users
Right-wing parties may appear on the wrong side of axes
Components may be mislabeled as "left" when they're actually "opposition"
Users get incorrect information about party positions

When to Apply

Apply this guidance when:

Creating or updating SVD/PCA component labels
Interpreting dimensionality reduction results in voting analysis
Building voting compasses or similar political guidance tools
Analyzing roll call votes or legislative voting data

Examples

Wrong Approach (Semantic)

# ❌ BAD: Based on motion topics, not voting patterns
SVD_THEMES = {
    1: {
        "label": "Sociale zekerheid vs economische liberalisering",
        # Reality: 9 coalition parties vote same way, 6 opposition vote opposite
    }
}

Correct Approach (Voting Patterns)

# ✅ GOOD: Based on actual voting behavior
SVD_THEMES = {
    1: {
        "label": "Rechts kabinetsbeleid vs links oppositiebeleid",
        "explanation": (
            "Deze as scheidt het kabinetsbeleid van de oppositie. "
            "9 coalitiepartijen stemmen aan de positieve kant, "
            "6 oppositiepartijen aan de negatieve kant."
        ),
    }
}

Verification Approach

To verify SVD component labels, check which parties have positive vs negative loadings on each component:

# From explorer.py - check party loadings for each component
for comp_num in range(1, 11):
    component_parties = svd_scores[svd_scores['component'] == comp_num]
    positive_parties = component_parties[component_parties['loading'] > 0]['party'].tolist()
    negative_parties = component_parties[component_parties['loading'] < 0]['party'].tolist()
    
    print(f"Component {comp_num}:")
    print(f"  Positive ({len(positive_parties)}): {positive_parties}")
    print(f"  Negative ({len(negative_parties)}): {negative_parties}")

Use this to verify:

Right-wing parties (PVV, FVD, JA21, SGP) appear on the correct side
The label matches the voting pattern, not just the topic

SVD Label Unification Plan
SVD Label Unification Design
Commits: 33edb33, e77f0ec, bfe37c6, f7fc908, 92c3c0e

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