Quelles caracteristiques du capital humain predisent le mieux les gains des immigrants de la composante economique
Bibliographic record
Abstract
Une abondante litterature traite du lien entre les caracteristiques des immigrants et leurs gains au Canada, mais les connaissances sont limitees en ce qui a trait a l'importance relative de divers facteurs lies au capital humain comme la langue, l'experience de travail et le niveau de scolarite pour predire les gains des immigrants de la composante economique. La diminution des gains des immigrants depuis les annees 1980, laquelle etait concentree chez les immigrants de la composante economique, a favorise des changements dans le systeme de points au debut des annees 1990 et en 2002, principalement dans le but d'ameliorer les gains des immigrants. Quand vient le temps d'effectuer de tels changements, il est important de connaitre le role relatif des differentes caracteristiques qui servent a determiner les gains des immigrants. Le present document porte sur deux questions. Tout d'abord, quelle est l'importance relative de facteurs observables lies au capital humain pour predire les gains des immigrants de la composante economique (demandeurs principaux) qui sont selectionnes par le systeme de points? Ensuite, l'importance relative de ces facteurs varie-t-elle a court, moyen ou long terme? Cette recherche est fondee sur la Base de donnees longitudinales sur les immigrants (BDIM) de Statistique Canada.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".