Political Parties in Canada: What Determines Their Entry, Exit and the Duration of Their Lives?
Bibliographic record
Abstract
In this paper we consider two margins of individual political party life in Canada since Confederation— \nthe extensive margin governing existence (the entry and exit decisions, together with party turnover or \nchurning) and the intensive margin determining lifespan or survival length. The results on the extensive \nmargin confirm in a more formal way many of the individual hypotheses advanced in the political \nliterature for entry and exit—the importance of voter heterogeneity, minority governments, world wars, \nnumber of competitors and economic circumstances. What stands out most strongly in the data is the \nintroduction of public funding for established political parties following 1974 and recent immigration \nflows. The intensive margin is explored using a number of hazard models before narrowing choice to \nsemi-parametric models. Potential endogeneity is dealt with by using a discrete hazard model with \ndiscrete finite mixtures. This form best captures the empirical hazard, allowing for the detection of party \ntype heterogeneity while being agnostic with respect to the correlation between observables and this \nspecific type of heterogeneity. The results suggest the presence of two distinct political party types and, \nmore generally, mirror the results found on the extensive margin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".