Racial inequality in employment in Canada: Empirical analysis and emerging trends
Bibliographic record
Abstract
Abstract: This article examines racial discrimination in employment in Canada using data from a sample of legal cases that were published in the Canadian Human Rights Reporter between 1980 and 1999. The authors discuss some theoretical perspectives on racial discrimination, briefly review empirical studies on the topic, examine the nature of and trends in such employment discrimination cases over the two decades, and provide an in‐depth discussion and analysis of selected legal cases on racial discrimination in Canada. After some concluding remarks, policy recommendations to combat racial discrimination in the workplace are suggested. Sommaire: Le présent article examine la discrimination raciale en matière d'emploi au Canada à l'aide de données d'un échantillon de causes judiciaires qui ont été publiées dans le Canadian Human Rights Reporter de 1980 à 1999. Les auteurs discutent de certaines perspectives théoriques sur la discrimination raciale, passent brièvement en revue les études empiriques sur le sujet, examinent la nature et les tendances de tels cas de discrimination en matière d'emploi au cours des deux décennies, et fournissent une discussion et analyse approfondie de causes judiciaires sélectionnées portant sur la discrimination raciale au Canada. En conclusion, ils proposent des recommandations de politiques pour combattre la discrimination raciale dans le lieu de travail
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".