The "Negative" Assimilation of Immigrants: A Counter-Example from the Canadian Labour Market
Bibliographic record
Abstract
With 2006 Canadian Census data, this paper investigates the effects of the number of years since migration (YSM) and the location of study on the economic performance of English-speaking immigrants from the U.S. and the U.K. in Canada. The aim is to test whether the “negative assimilation” hypothesis proposed by Chiswick and Miller (2011) is a universal finding for immigrants from countries with similar economic standing and skill transferability to those of the destination country. Geographical, demographic, language, immigration and educational characteristics are taken into account in the models. The regressions are done for both males and females. This study finds that the negative assimilation hypothesis does NOT hold for the Canadian labour market. Specifically, the assimilation rate is close to zero for U.K. immigrants and strictly positive for U.S. immigrants (although lower than that of a comparison group of Chinese immigrants). Furthermore, the U.S. immigrants who hold a degree from the U.S. earn lower wages than those who studied in Canada, while this relationship is not significant for the U.K. immigrants.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".