Retourner sa veste au Québec : une étude des défections transpartisanes politiques de 1980 à 2018
Bibliographic record
Abstract
Résumé Cet article aborde le phénomène des défections dans les partis politiques grâce à une étude du cas des députés québécois. Les entrevues avec les anciens parlementaires et une analyse des discours médiatiques à propos de leur départ documentent les motivations à quitter ou à rejoindre un autre parti. Cette recherche propose deux typologies novatrices : la première identifie les types de défections et la seconde explore les motivations à quitter. Les analyses montrent que les députés québécois partent surtout en raison de conflits liés aux valeurs et aux politiques, mais très peu pour le prestige. En ouvrant un dialogue avec les travaux d'Hirshman, l'article illustre aussi le rôle de la prise de parole et de l’écoute dans la décision pour un député de quitter un parti ou de rester dans celui-ci.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".