University-Educated Immigrants from China to Canada: Rising Number and Discounted Value
Bibliographic record
Abstract
Economic globalization has changed the volume and nature of the world migrant population. This paper explains how and why the pattern of immigration from China to Canada has changed as a result of a rising demand for human capital in Canada and a growth in the supply of university graduates in China. Since 1998, China has emerged as the top immigrant source for Canada, and there is a higher human capital content among recent cohorts of China-born immigrants to Canada. However, evidence from the 2001 Census of Canada indicates that the return on the university credentials of China-born immigrants in many fields of study has been below the Canadian average. The findings suggest that there are severe devaluations of the foreign credentials of China-born immigrants in Canada; the devaluation is more severe for Chinese men than Chinese women. La mondialisation économique a changé le volume et la nature de la population migrante de la planète. Dans cet article, nous montrons comment et pourquoi le type d'immigration de Chine vers le Canada s'est transformé du fait de l'augmentation du nombre de détenteurs de diplômes universitaires d'une part, et de la demande croissante de capital humain de l'autre. Depuis 1968, la Chine est devenue la source principale de l'immigration au Canada, et c'est dans les rangs de ces immigrants nés en Chine qu'on trouve l'investissement le plus élevé dans le capital humain. Cependant, le recensement canadien de 2001 indique que, dans un grand nombre de domaines, le retour sur les crédits universitaires d'immigrants nés en Chine est inférieur à la moyenne de celui des Canadiens. Ce que l'on constate suggère qu'il se produit une sévère dévaluation lors de la validité de diplômes étrangers des immigrants canadiens venus de Chine, et que cette dévaluation est encore plus grave pour les hommes que pour les femmes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".