Bibliographic record
Abstract
We use quantile regression methods on 2001 Census of Canada data to assess disparity at four points in the conditional distribution of earnings of native-born ethnic minorities (the 20th, 50th, 80th and 90th percentiles) as well as at the mean. In doing so, we examine and assess the degree to which minorities face earnings differentials at both the top and bottom of the conditional distribution as well as at the mean, thereby testing the degree to which the mean difference is representative of differences across the distribution. We consider glass ceilings for Canadian-born ethnic minorities, and find evidence that some groups, such as Chinese-origin people, do indeed face more earnings disparity at the top of the distribution. However, other groups face different structures. South Asian-origin workers face greater disparity at the bottom than at the top, and Black workers face great disparity across the distribution. We interpret these latter patterns as identifying poor access of minority workers to good jobs in various parts of the distribution, rather than as negating a glass ceiling.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".