L’embauche dans les départements de science politique francophone au Québec et au Canada. Un bilan des années 2000-20101
Bibliographic record
Abstract
Entre 2000 et 2010, les départements de science politique des universités francophones du Québec et du Canada (Université Laval, Université de Montréal, Université du Québec à Montréal et Université d’Ottawa) ont embauché 81 nouveaux professeurs. Quelles étaient les caractéristiques de ces professeurs au moment de leur embauche ? Quel était leur parcours ? Où ont-ils obtenu leur doctorat ? Quelle est leur langue d’usage ? Dans quelles revues ont-ils publié avant leur embauche ? Quel était leur âge au moment de l’embauche ? Quelle a été l’importance du genre, du statut au Canada et de l’origine ethnoculturelle lors de ces embauches ? Cette étude répond à ces questions en se basant sur le curriculum vitae que ces professeurs ont envoyé au comité d’embauche. Les données fournissent certaines indications sur l’évolution de la discipline.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".