Nouvelles pratiques de travail et taux de demissions : problemes methodologiques et donnees empiriques pour le Canada
Bibliographic record
Abstract
A l'aide d'un echantillon national representatif d'etablissements, nous avons cherche a determiner si l'adoption de certaines pratiques de travail equivalentes (PTE) a tendance a reduire le taux de demissions. Dans l'ensemble, notre analyse fournit des preuves solides d'une association negative entre l'adoption de certaines PTE et le taux de demissions, pour les etablissements comptant plus de dix employes du secteur des services hautement specialises. Nous degageons aussi certaines preuves d'une association negative pour le secteur des services peu specialises. Cependant, la force de cette association negative diminue considerablement lorsque nous ajoutons un indicateur precisant si l'etablissement a adopte ou non une politique officielle de partage de l'information. Dans le secteur de la fabrication, les preuves d'une association negative sont faibles. Bien que les etablissements ayant des groupes de travail autonomes aient affiche un taux de demissions plus faible que les autres, aucun ensemble de pratiques de travail etudie n'a d'effet negatif et statistiquement significatif sur ce taux. Nous emettons l'hypothese que les PTE cles peuvent reduire davantage le roulement de la main-d'oeuvre dans des environnements techniquement complexes que dans des environnements requerant peu de competences.
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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 | 0.048 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.026 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".