The effect of linguistic proximity on the occupational assimilation of immigrant men in Canada
Bibliographic record
Abstract
This paper contributes to the analysis of the integration of immigrants in the Canadian labour market by focusing in two relatively new dimensions. We combine the large samples of the restricted version of the Canadian Census (1991-2006) with both a new measure of linguistic proximity of the immigrant's mother tongue to that of the destination country, and with information of the occupational skills embodied in the jobs immigrants hold. This allows us to assess the role that language plays in the labour market performance of immigrants and to better study their career progression relative to the native born. Weekly wage differences between immigrants and the native born are driven mostly by penalties associated with immigrants' lower returns to social skills, but not to analytical or manual skills. Interestingly, low linguistic proximity between origin and destination language imposes larger wage penalties to the university-educated, and significantly affects the status of the jobs they hold. The influence of linguistic proximity on the skill content of jobs immigrants hold over time also varies by the educational level of the migrant. We also show that immigrants settling in Quebec and whose mother tongue is close to French have similar or better labour market outcomes (relative to native-born residents in Quebec) than immigrants with close linguistic proximity to English settling outside Quebec (relative to native born residents in the rest of Canada). However, since wages in Quebec are lower than elsewhere, immigrants in Quebec earn less in absolute terms than those residing elsewhere.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".