L'évaluation des directions des ressources humaines dans le secteur public québécois
Bibliographic record
Abstract
Cet article présente les résultats d'une recherche empirique sur l'évaluation de l'efficacité des directions des ressources humaines (DRH) dans le secteur public québécois selon l'approche par les clients. Cette méthode mesure l'efficacité des DRH par la satisfaction de leurs clients. Le modèle proposé et testé distingue les attentes et la satisfaction des clients (contrairement aux travaux précédents) et tient compte des effets des caractéristiques des DRH sur l'évaluateur (le client). Les résultats de l'étude confirment globalement les grandes conclusions de Tsui (1987, 1990) quant à l'existence de différences significatives dans la satisfaction et les attentes des clients tout en apportant quelques modifications méthodologiques. Aussi, de façon globale, les conclusions mettent en relief l'effet de trois variables indépendantes (l'engagement des clients, la compétence des membres des DRH, la fréquence des contacts des clients avec leur DRH) sur la satisfaction des clients selon les deux axes « relations du travail » et « gestion des ressources humaines ».
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".