Unity in Diversity? The Development of Political Parties in the Parliament of Canada, 1867–2011
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
What explains the development of legislative party voting unity? Evidence from the United States and Britain indicate that partisan sorting, cohort replacement effects, electoral incentives, and agenda control contributed to enhancing party cohesion during the nineteenth and twentieth centuries. Here, these mechanisms are evaluated by analysing a dataset containing all the recorded votes from the Canadian House of Commons, 1867–2011. Overall, we find that partisan sorting and the government’s ability to control the agenda are central to the consolidation of parties over time. Our results underscore the need to integrate institutional rules and legislative agendas into models of parliamentary voting behaviour and suggest that strict party discipline can lead to the development of a multi-party system in the legislative arena.
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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 | 0.007 | 0.002 |
| 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 | 0.001 | 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 it