Intelligence artificielle et transformation de la GRH : vers une reconceptualisation de l’efficacité des systèmes RH
Bibliographic record
Abstract
La recherche en gestion stratégique des ressources humaines (GRH) s’est traditionnellement concentrée sur le contenu, soit sur les pratiques mises en place au sein des organisations. Cette perspective domine également les travaux récents sur l’intelligence artificielle (IA), principalement axés sur la transformation des tâches et des activités. Toutefois, une approche émergente met l’accent sur le processus, offrant une meilleure compréhension de la structuration des systèmes RH et des signaux transmis aux personnes en emploi. Dans cette logique, cet article propose un modèle conceptuel montrant comment les paramètres d’une IA responsable — fiabilité, sécurité et confiance — influencent les dimensions du processus RH — distinctivité, cohérence et consensus — et façonnent les signaux perçus en matière d’équité. L’étude contribue ainsi à la littérature et propose des recommandations pour orienter les recherches futures.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".