Sélectionner des candidats en toute légalité
Bibliographic record
Abstract
Résumé Comment l’organisation peut-elle s’assurer qu’elle ne sélectionne pas des candidats sur la base d’informations discriminatoires? Entre les tests, les entrevues, la prise de références et l’examen médical, que doit-elle choisir afin de se prémunir contre des poursuites éventuelles? Voilà autant de questions relatives à la sélection du personnel qui trouvent un écho dans les lois. Cet article vise à informer les employeurs sur le cadre légal québécois qui s’applique à la sélection (particulièrement les droits fondamentaux des candidats, l’interdiction de la discrimination en emploi et la protection des renseignements personnels). Il fait ressortir l’importance de déterminer clairement les exigences du poste à pourvoir pour qu’en découle un choix avisé d’outils d’évaluation, d’appliquer ces outils rigoureusement et avec discernement et de minimiser le recours aux méthodes d’évaluation intrusives. Fonctions : GRH, management, droit, international
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".