Surqualification et sous-qualification des travailleurs sur le marché du travail : le cas du Québec et de l'Ontario en 1991 et 1996
Bibliographic record
Abstract
This text investigates the causes of the noticeable difference between the education level reached by workers and the level of education required to do their job. This issue is important in order to appreciate the adequacy of the relationship between workplace requirements and the education system. It is observed that the over-qualified workers are usually younger workers. The older workers compensate for their formal lack of education by having more experience. The labor market in Ontario has a better adequacy than the one in Quebec. The outcome of over-qualification or under-qualification is real. Over-qualified individuals are paid more than people who have the proper education for their job. On the other hand, under-qualified individuals are usually paid less. Ce texte examine les raisons pour lesquelles on observe une différence entre le niveau de scolarité atteint par les travailleurs et le niveau requis par l'emploi. Cette question est importante pour apprécier l'adéquation entre le marché du travail et le système éducatif. On remarque que les surqualifiés sont généralement les jeunes travailleurs. Les travailleurs plus âgés compensent le manque de formation par plus d'expérience. L'adéquation est meilleure sur le marché du travail ontarien que québécois. L'effet de la surqualification ou de la sous-qualification sur les salaires est réel. Les individus surqualifiés ont une rémunération plus importante et les individus sous-qualifiés moins importante que les personnes qui ont la formation adéquate pour leur emploi.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 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 | 0.000 |
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".