Prédiction du risque de vulnérabilité des unités de travail dans les organisations
Bibliographic record
Abstract
Ce papier propose une méthode de prédiction du risque de vulnérabilité des unités de travail au sein d’une organisation. Le contrôle dynamique du risque de vulnérabilité est un enjeu pour la direction des ressources humaines, soucieuse de garantir une grande efficacité organisationnelle et d’accroître la productivité et la compétitivité de l’organisation. Les indicateurs de mesure de vulnérabilité sont combinés pour construire des indices de risque de vulnérabilité. L’approche utilisée est quantitative, elle s’appuie sur des modèles logits. L’intérêt de l’étude est de mettre au service de la politique sociale de l’entreprise, un outil de prévision et d’aide à la décision. Les données d’applications sont celles de la Banque Nationale du Canada et celles de l’enquête « Regard sur notre organisation ».
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".