La mobilisation du personnel : l'art d'établir un climat d'échanges favorable basé sur la réciprocité
Bibliographic record
Abstract
Résumé Savoir ce qu’ont en commun les organisations efficaces et performantes, découvrir la recette de leur succès, est plus que jamais un sujet de fascination dans la communauté d’affaires et l’objet de vifs débats dans le milieu universitaire. Pourquoi certaines organisations réussissent-elles mieux que d’autres? Parmi les réponses apportées, deux se détachent. D’abord, les entreprises performantes ont réussi à devenir des employeurs de choix en établissant des relations très positives avec leurs employés. Ensuite, ces employeurs ont pu susciter des comportements de mobilisation sur une grande échelle. Bref, ces organisations ont su mettre en place un climat organisationnel mobilisateur. Cet article poursuit deux objectifs : proposer un modèle rigoureux et intégrateur de la mobilisation, et déterminer et définir les conditions psychologiques essentielles à un climat propice à la mobilisation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".