Survol des contributions théoriques et empiriques liées au capital humain
Bibliographic record
Abstract
Dans cette étude, nous passons en revue de façon exhaustive les développements majeurs en commençant, comme il se doit, par la contribution fondamentale de Becker (JPE '62 et Woytinski Lecture '67). Nous portons ensuite notre attention sur les travaux de Mincer (JPE '58, '62 et NBER '74) qui, plus que quiconque, contribua à l’application pratique des concepts théoriques. Tout naturellement, l’accent est mis sur le développement de « sa » fonction de gains quoique nous soulignions aussi la compréhension précoce par Mincer des problèmes d’hétérogénéité individuelle. Nous enchaînons ensuite avec les développements plus récents tant du point de vue théorique (matching, modèle de paiements retardés de Lazear), que du point de vue empirique. À cet égard, nous examinons à la fois les contributions ayant fait usage des mesures directes d’accumulation du capital humain (e.g. nombre de semaines passées en formation) ainsi que celles n’ayant à leur disposition que les mesures indirectes habituelles (expérience, ancienneté).
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".