La valorisation des ressources humaines en gestion stratégique des ressources humaines
Bibliographic record
Abstract
Cette recherche apporte un éclairage théorique important sur l'écart qui existe actuellement entre le discours des dirigeants et les réalités organisationnelles en ce qui concerne l'importance des ressources humaines comme source d'avantage concurrentiel, en intégrant à un modèle de gestion stratégique des ressources humaines, une variable encore très peu étudiée, soit la valorisation des ressources humaines.Nous proposons en effet un modèle alternatif à l'approche comportementale de Schuler et Jackson (1989).Nos résultats apportent ainsi une confirmation au fait que certaines stratégies d'affaires comportent des exigences de valorisation des ressources humaines plus grandes que d'autres, et qu'en fonction de ces exigences, diverses pratiques de gestion des ressources humaines sont mises en place.Mots clés :
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".