L'incidence des caracteristiques d'une universite sur les resultats professionnels apres le diplome : temoignages de trois cohortes canadiennes recemment diplomees
Bibliographic record
Abstract
Le present document modelise les revenus des hommes et des femmes titulaires d'un baccalaureat au Canada, cinq ans apres l'obtention de leur diplome. Au moyen d'une approche d'effet constant au niveau de l'universite, les chercheurs ont observe des variations importantes (constantes) au chapitre des revenus des finissants de differentes universites. La variation des caracteristiques des universites au fil du temps sont mises en correlation avec la variation des revenus des finissants. L'augmentation du taux d'inscription aux programmes universitaires de premier cycle est liee a une reduction des revenus subsequents des finissants, ce qui laisse supposer une saturation du marche. Chez les hommes, mais pas chez les femmes, les augmentations du rapport professeur-etudiant sont associees a une augmentation appreciable des revenus subsequents des etudiants. Les modeles qui ne tiennent pas compte de la majeure des etudiants affichent une incidence accrue de la variation des caracteristiques des universites, les effets atteignant pres du double de leur valeur initiale. Chez les femmes en particulier, la variation de plusieurs caracteristiques des universites est etroitement liee a la variation du choix de majeure. La variation des caracteristiques des universites n'est pas etroitement liee a la probabilite d'avoir un emploi cinq ans apres l'obtention du diplome.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".