Employment Probability of Visible Minority Immigrants in Canada by Generational Status, Circa 2016
Bibliographic record
Abstract
Using 2016 Census data, we compare the odds of employment (full time or self-employment) for visible minority immigrants in Canada with those of non–visible minority immigrants. Intergenerational comparisons of employment outcomes are made because one would expect second- and third-generation immigrants to be less prone to labour market barriers than first-generation immigrants. Estimates based on a logistic regression of employment probability reveal lower employment odds for four out of 10 identified visible minority immigrant groups in comparison with non–visible minority immigrants for all three generations. For first- and second-generation immigrants, the results were mixed, but third-generation immigrants faced significantly lower employment probabilities in all groups of visible minorities with the exception of Chinese and Japanese. A lack of proficiency in official languages (English or French) lowers the employment probability for all groups. It is estimated that post-secondary education (PSE) acquired outside of Canada has a weaker positive association with employment than PSE acquired within Canada. Pre- and post-immigration labour market experience have a weak association with employment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".