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Recorded-vote analysis · Texas House 89R
Technical case study

Modeling Texas House voting patterns beyond raw agreement

A self-directed demonstration of how Polilens can normalize a full legislative vote record, model member-specific baselines, and turn the results into inspectable network views.

Allen Ye

Founder & Software Engineer

founder@polilens.io

87

modeled Republicans

1,586

qualifying divided GOP rolls

1,245

modeled GOP connections

34

cross-party pairs

The analytical problem

Raw agreement rewards easy votes and members who usually vote with everyone.

This demonstration asks which voting relationships exceed what a member's normal behavior and each roll call's difficulty would predict.

01

Normalize the record

Resolve members, roll calls, bill context, and valid vote positions across the session.

02

Model expected behavior

Estimate member- and roll-specific baselines instead of treating every agreement equally.

03

Test the relationships

Apply effect-size, uncertainty, multiple-testing, and bootstrap gates to each pair.

04

Make the result inspectable

Open portraits and connections to see the members, direction, and contributing roll calls.

Preview of the Republican voting-cohesion network

Within-party model

Republican voting cohesion

Which Republican pairs vote on the same side more often than member-specific and roll-adjusted baselines predict?

3,741
pairs tested
1,245
modeled connections
201
default display
Preview of the directional cross-party voting patterns

Republican ↔ Democrat

Directional cross-party voting patterns

Which Republican and Democratic pairs appear on the other party’s majority side more often than party-specific model baselines predict?

905
opposed-majority rolls
34
modeled pairs
48
modeled directions
Client relevance

The same workflow can answer organization-specific legislative questions.

The useful output is not limited to a network. The underlying process supports caucus analysis, issue coalitions, target-member research, vote-context reconstruction, and session-level behavioral briefs.

Legislative member and vote data derived in part from LegiScan datasets, licensed under CC BY 4.0. Polilens transformed the source data and produced the analysis shown here.