Measuring and Mismeasuring Discrimination against Visible Minority Immigrants: The Role of Work Experience
Bibliographic record
Abstract
English There are two methods for estimating the earnings disadvantage of groups: the residual differencemethod and the Oaxaca-Blinder decomposition. Each method infers disadvantage fromdifferences in earnings of visible minority immigrants and other Canadians, after controlsfor human capital and job characteristics. We: i) summarize the logic of these methods; ii)critically examine the character of the experience measures used in most of the research; iii)apply the residual difference method to the Workplace and Employee Survey to show how amore thorough approach to the measurement of work experience modifies estimates of earningsdisadvantage. French Deux méthodes sont utilisées pour estimer le désavantage salarial de groupes : la ‘méthode dedifference résiduelle’ et la ‘décomposition Oaxaca-Blinder’. Selon la logique de ces méthodes,après avoir contrôlé pour les différences du capital humain, le désavantage des immigrants deminorités visibles relatifs aux autres Canadiens est la différence nette du salaire. Dans cettearticle nous : i) décrivons la logique de ces méthodes ; ii) examinons la qualité des indicateursd’expérience utilisés ; iii) analysons les données de l’Enquête sur le milieu du travail et lesemployés. Notre conclusion est que le fait d’inclure d’indicateurs améliorés de l’expérience apour effet de modifier l’estimation du désavantage.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".