When the team’s jersey is what matters: Network analysis of party cohesion and structure in the Canadian House of Commons
Bibliographic record
Abstract
Are parties “high discipline, low cohesion” in Westminster legislatures? This study applies network analysis to voting behavior among members of parliament (MPs), a novel approach that measures not deviation from party-line voting, but rather whether MPs with similar voting patterns are co-partisans. We study the Canadian Parliament from 2006 to 2015, during which time the governing party under Prime Minister Stephen Harper maintained tight central control and discipline, a likely source of elevated cohesion. We find that “low cohesion” generally holds, and parties do not always conform to commonsense expectations about how cohesively they “should” behave in various parliamentary situations, though they show themselves capable of learning over time. Moreover, we find that party cohesion stems less from shared voting behaviors and more from simple partisan identity. Further research should consider to what extent parliamentary behavior is based mainly on party alignment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".