Decomposing Immigrants’ Economic Integration in Earnings Disparity: Racial Variations in Unexpected Returns
Bibliographic record
Abstract
Le discours sur l’intégration économique des immigrés prend souvent pour acquis que le capital humain de ces derniers détermine le succès de leur intégration, tel que comparé aux revenus de la population d’origine. Dans cet article, nous décomposons les salaires des uns et des autres au Canada pour voir à quel point on peut attribuer la disparité de gains au capital humain en question ou à d’autres facteurs. Selon nos résultats, les immigrés avaient à leur arrivée une formation et une expérience de haut niveau, souvent supérieures à celles des Canadiens de naissance, mais ils ont été sous-rétribués à cause de l’absence d’autres bénéfices non expliqués. Ceci suggère que la politique de sélection des immigrants a des limites, et qu’il faudrait reconsidérer d’autres stratégies pour infléchir la manière dont le marché du travail les traite, surtout ceux d’origine minoritaire, afin de combler cette inégalité de revenu.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".