La dotation dans le contexte de la diversité culturelle : enjeux et recommandations
Bibliographic record
Abstract
Résumé La dotation dans le contexte de la diversité culturelle est devenue un véritable défi pour les organisations. Afin de venir en aide aux gestionnaires et aux spécialistes des ressources humaines, cet article commence par clarifier deux phénomènes fondamentaux en matière d’évaluation des compétences : les biais culturels et l’effet défavorable. La première partie de l’article définit ce qu’est un outil d’évaluation non biaisé sur le plan culturel et montre la difficulté à démontrer ce fait. La deuxième partie présente quatre outils d’évaluation utilisés dans la sélection du personnel pour prédire le rendement dans l’emploi et leur impact négatif sur les minorités culturelles. Enfin, la troisième partie propose des recommandations de nature à aider les organisations à appliquer un processus de sélection équitable.
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 | 0.037 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".