Ces cadres-vedettes qui deviennent des cas problématiques : comment prévenir ce risque ?
Bibliographic record
Abstract
Résumé L’intensification de la concurrence oblige les dirigeants à recruter pour les postes clés des candidats très doués, ambitieux et créatifs pouvant améliorer de façon importante les résultats de l’entreprise. Cependant, si ces cadres-vedettes constituent un précieux capital humain, dans certains cas ils peuvent se montrer arrogants, trop avides de succès et de reconnaissance ; leurs comportements risquent alors de nuire au climat de travail, voire à l’atteinte des objectifs de l’entreprise. Cet article dresse un portrait de ces vedettes dont la conduite est inacceptable. Nous décrivons ensuite les défis qu’entraîne leur arrivée dans l’entreprise, en insistant sur les effets nuisibles à la performance collective. En vue de minimiser ces risques, nous soumettons enfin aux dirigeants et aux gestionnaires des recommandations dont l’efficacité est reconnue par les chercheurs et les praticiens.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".