Les entreprises de haute technologie et leurs pratiques de recrutement, de sélection, d'évaluation du rendement et de rémunération
Bibliographic record
Abstract
Résumé L’objectif de cette recherche est d’analyser des cas types permettant de dresser un bilan des pratiques de gestion des ressources humaines dans les entreprises de haute technologie. Quatre thèmes sont couverts dans le présent article : le recrutement, la sélection, l’évaluation du rendement et la rémunération. La première partie de l’article examine les défis qui se présentent actuellement dans ces quatre domaines. La deuxième partie expose la méthodologie retenue pour réaliser la recherche. Les quatre parties suivantes se penchent sur les méthodes et les stratégies de recrutement, de sélection, d’évaluation du rendement et de rémunération qu’on observe dans les entreprises de haute technologie selon leur taille. Cette présentation est suivie d’une discussion et de recommandations.
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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 | 0.029 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".