Bibliographic record
Abstract
We study the role of credentials or ‘sheepskin effects’ in the Canadian labour market. Sheepskin effects refer to increases in wages associated with the receipt of a degree after controlling for educational inputs such as years of schooling. We find strong evidence of sheepskin effects associated with graduation from high school, community college or trade school, and university. The importance of credentials increases with educational attainment, accounting for 30 per cent of the return to 16 years of schooling but more than half of returns above 16 years. Our evidence indicates that both years of schooling and degree completion influence earnings. JEL Classification: I2, J3 Le rôle des certificats dans le marché du travail au Canada. Les auteurs étudient le rôle des certificats ou des «effets de parchemin» dans le marché du travail au Canada. Les effets de parchemin font référence aux augmentations de salaires associées à l’obtention d’un diplôme ou d’un grade après avoir pris en compte l’expérience éducationnelle comme les années d'éducation. On découvre des résultats probants pour ce qui est des effets de parchemin associés à l’obtention d’un diplôme d'école secondaire, de collège communautaire, d'école technique, et d’université. L’importance de la certification augmente avec le niveau d’instruction : elle compte pour 30 pourcent du rendement pour 16 années de scolarité mais pour plus de la moitié des rendements pour la scolarité au delà de ces premiers 16 ans. Les résultats indiquent que tant les années de scolarité que la certification comme telle ont un impact sur les gains.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".