Les effets de cohorte sur les gains annuels selon le domaine d'etudes des diplomes universitaires de la Colombie-Britannique
Bibliographic record
Abstract
A l'aide d'un ensemble de donnees qui combine les dossiers fiscaux de 1982 a 1997 et les dossiers administratifs des diplomes au niveau du baccalaureat de Colombie Britannique des annees de promotion comprises entre 1974 et 1996, nous examinons dans la presente etude les gains d'emploi annuels reels des diplomes de 20 principaux domaines d'etudes afin de cerner les variations significatives des diverses cohortes. Les diplomes de sexe masculin des cohortes plus recentes affichent des gains d'emploi moyens plus faibles apres l'obtention du diplome mais un meilleur rendement de l'experience. Les diplomes de sexe feminin des cohortes recentes affichent des niveaux de gain d'emploi egaux apres l'obtention du diplome et un meilleur rendement de l'experience. Les gains d'emploi moyens different selon le domaine d'etudes, les plus eleves etant ceux des diplomes des domaines de la formation des enseignants, du commerce, du genie, des sciences infirmieres et des sciences medicales, mais les effets de cohorte sont statistiquement identiques dans le cas des diplomes de tous les domaines d'etudes. Ces resultats ne fournissent aucune preuve d'une variation importante des gains d'emploi conforme a une diminution du rendement des etudes universitaires ou une reorientation de la demande favorisant certains diplomes.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".