Bibliographic record
Abstract
La contribution économique des immigrants est mesurée par l'am‐pleur de leurs salaires. Plus on diminue l'écart des salaires, plus les immigrants sont sensés se doter du capital humain. En utilisant les données du recensement de 1996, cet article compare des groupes d'immigrants avec des Canadiens de naissance de même sexe et de même origine raciale à quatre niveaux de la région métropolitaine de recensement, définie par la taille de la population. Les résultats indiquent que les immigrants de même sexe et de même origine raciale gagnent soit le même salaire sinon plus que leurs homologues canadiens. Cependant, en prenant en considération les variations dans le capital humain, l'expérience, les différences dans l'échelle urbaine, la taille de la population immigrante et le taux de chômage, tout groupe d'immigrants gagne moins que son homologue canadien. L'ampleur des salaires nets entre les immigrants et les Canadiens de naissance varie selon le sexe, l'origine raciale et moins ainsi selon le niveau de la région metropolitaine de recensement. Plusieurs fac‐teurs, dont les possibilités d'emploi inégales, touchent le salaire des immigrants. II n'est pas du tout évident de supposer que la teneur du capital humain des immigrants est inférieure alors qu'elle est déduite de la disparité de salaires. The economic contribution of immigrants is often measured by their earnings in that the closer they are to the earnings of native‐born Canadians and the more quickly immigrants can bridge the income gap, the more immigrants are assumed to be endowed with human capital. Using microdata of the 1996 census, this paper compares immigrant groups with native‐born Canadians of the same gender and racial origin at four levels of Census Metropolitan Area defined by population size. The findings indicate that immigrants of the same gender and racial origin earned either the same or more than their native‐born counterparts. However, when variations in human capital, experience, and other individual differences in work‐related characteristics and immigrant experience are taken into account, along with differences in urban scale, immigrant population size and unemployment rate, all immigrant groups earned less than their native‐born counterparts. The magnitude of net earning disparities between immigrants and native‐born Canadians varies, depending on gender, racial origin and less so on CMA level. The study suggests that many factors, including unequal opportunities, affect the earnings of immigrants, and that the assumption of immigrants' inferior human capital content inferred from earning disparities is tenuous at best.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".