Les critères de sélection de la main-d’œuvre et le jugement sur les compétences des candidats à l’embauche au Canada. Quelques éléments d’analyse
Bibliographic record
Abstract
Dans un contexte marque par des changements acceleres et par des incertitudes multiples et diverses sur les qualites de la main-d’œuvre, le recrutement des nouveaux employes devient de plus en plus complexe, notamment dans des entreprises ou l’organisation du travail est en profonde mutation. A partir des resultats d’une enquete qualitative aupres de trente entreprises canadiennes situees dans la region de Quebec, cet article analyse 1) les criteres de selection de la main-d’œuvre utilises par les entreprises qui ont fait l’objet de la recherche, en mettant l’emphase sur le role du diplome, de l’experience professionnelle et des qualites individuelles ; 2) la formation du jugement sur les competences des candidats a l’embauche dans le processus de recrutement. Nous montrons que le diplome perd progressivement sa valeur sur le marche du travail et que l’experience est devenue une composante naturalisee de la competence dont le contenu varie en fonction du jugement subjectif des recruteurs.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".