Ethnic Inequality in Canada: Economic and Health Dimensions
Bibliographic record
Abstract
This study examines ethnic based differences in economic and health status. We combine existing literature with our analysis of data from the Canadian Census and National Population Health Survey. If a given sub-topic is well researched, we summarize the findings; if, on the other hand, less is known, we present data placing them in the context of whatever literature does exist. Our findings are consistent with existing literature on ethnic inequalities in Canada. Recent immigrants with a mother tongue other than English or French are among the most economically disadvantaged in Canadian society, though the results vary depending on gender and ethnic background. In fact economic inequality according to type of occupation can be attributed to gender rather than ethnicity; that is, the Canadian labour force continues to be more gender- than ethnically-differentiated. Yet recent immigrants, especially from Asia, are advantaged in health outcomes compared to Canadian-born persons – the “healthy immigrant” effect. Interestingly they are less likely to report having a physical check-up and, for women (especially Asian-born women), a mammogram within the last year compared to their Canadian-born counterparts. Given the significance of both gender and ethnicity as predictors of well-being, future research should examine the intersection between the two identity markers and their relationship to social inequality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".