Why is Immigrants' Access to Employment lower in Montreal that in Toronto?
Bibliographic record
Abstract
This paper explores reasons why the employment rate gap between immigrants and Canadian born individuals is larger in Montreal than in Toronto. A major reason is language: relative to Canadian born individuals, immigrants in Montreal are significantly less likely to know French than their Toronto counterparts to know English and their knowledge of French is less rewarded by employers than their Toronto counterparts’ knowledge of English. We also find that holding other factors constant, the performance of immigrants according to their countries of origin is remarkably similar in Montreal and Toronto: in both metropolitan areas, immigrants from Europe and India generally perform better than immigrants from China, Taiwan and Muslim countries. While we do not find any evidence that Quebec’s different immigration policy is causing the larger immigrant employment rate gap in Montreal, we cannot rule out the possibility that immigrants would be subject to more labour market discrimination in Montreal than in Toronto. However, this discrimination would be French language related as opposed to being ethnicity related. Results are generally similar for both male and female immigrants. / Ce document explore les raisons pour lesquelles l'écart de taux d'emploi entre les immigrants et les personnes nées au Canada est plus grand à Montréal qu'à Toronto. Une raison importante est la langue : par rapport aux Canadiens de naissance, les immigrants à Montréal sont beaucoup moins susceptibles de connaître le français que leurs homologues à Toronto de connaître l'anglais, et leur connaissance du français est moins récompensée par les employeurs que la connaissance de l’anglais des immigrants à Toronto. Nous trouvons également que, toutes choses égales par ailleurs, la performance des immigrants selon le pays d'origine est remarquablement similaire à Montréal et à Toronto : dans les deux régions métropolitaines, les immigrants de l'Europe et de l'Inde ont en général une meilleure performance que ceux de Chine, de Taïwan et des pays musulmans. Bien qu’il n’y ait pas de preuve que la politique d'immigration spécifique du Québec soit la cause du plus grand écart de taux d'emploi à Montréal, nous ne pouvons pas exclure la possibilité qu’il y ait une plus grande discrimination sur le marché du travail contre les immigrants à Montréal qu'à Toronto. Toutefois, cette discrimination serait liée à la langue plutôt qu’à l’ethnicité. Les résultats sont généralement similaires pour les hommes et 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".