Les rendements privés de la formation selon l'âge des travailleurs au Québec et comparaison avec l'Ontario
Bibliographic record
Abstract
In this report, we estimate returns to training (in terms of wages and productivity) using data from the Workplace and Employee survey from 199-2004 and investigate whether these returns vary with age. Although we find that the returns to training on wages is fairly constant across all age groups, we find that the returns to classroom training on productivity falls dramatically with age. For example, for Canada, we find that workers below 35 years of age who received training are 50% more productive relative to their non-trained counterparts. However, we also find that this differential falls to 5% for workers 55 years of age or older. This result suggests that the observed decreases in the incidence of training with age might be because firms first train workers for which productivity gains will be the highest. Nous estimons dans cette recherche les rendements de la formation parrainée par l'employeur à l'aide des données de l'Enquête sur le milieu de travail et les employés de Statistique Canada pour la période 1999-2004 et vérifions si ces rendements varient avec l'âge. Alors que nous trouvons que les rendements de la formation en termes de salaires varient très peu selon l'âge, nous observons une baisse marquée des rendements de la formation en classe sur la productivité du travailleur. Par exemple, pour le Canada, alors que le rendement de la formation estimé est d'environ 50 % pour les moins de 35 ans, nous estimons un rendement d'à peine 5 % pour le groupe des 55 ans et plus. Il suit donc que les baisses de l'incidence de la formation selon l'âge pourraient être expliquée par le fait que l'établissement offre tout d'abord la formation aux employés pour lesquels il retirera des gains de productivité plus élevés.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".