Implementation and Removal of an Affirmative-Action Quota: The Impact on Task Assignment and Workers’ Skill Acquisition
Bibliographic record
Abstract
Le Canada et les États-Unis ont des lois qui visent à réduire les inégalités en matière d’emploi et qui ciblent des groupes donnés de citoyens. Les États-Unis ont une longue tradition d’action positive, qui remonte à un décret promulgué par le président Kennedy en 1961; le Canada, pour sa part, a adopté en 1986 une loi sur l’équité en matière d’emploi. Les mesures d’action positive visant à réduire les inégalités en matière d’emploi ont toujours suscité beaucoup de controverse, et de nombreux cas ont été portés devant les tribunaux, ce qui a conduit des États et des provinces à abroger leurs lois. Coate et Loury (1993) ont analysé de façon théorique l’impact de ces actions positives, mais leurs résultats restent toutefois ambigus. Dans cet article, nous avons recours à une expérience de laboratoire pour éclaircir de façon empirique cette ambiguïté.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".