Training and the Earnings of Immigrant Males: Evidence from the <i>Canadian Workplace and Employee Survey</i><sup>*</sup>
Why this work is in the frame
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Bibliographic record
Abstract
Objective. To improve on the existing research on earnings differentials between visible minority immigrants and the native‐born, and on the role of discrimination in producing that difference. To do this we introduce into the analysis: (1) access to training and (2) training effects on earnings growth. Method. Using a panel data set containing information on training we test cross‐sectional models of access to training, cross‐sectional models of wage determination, and panel models of wage growth. Results. Visible minority immigrants are disadvantaged in both access to training and earnings; education reduces the disadvantage; and they do better than the other two groups in wage growth . Conclusions. Some results are consistent with a discrimination interpretation but, considered together, the complete sets of results are difficult to reconcile with any relatively straightforward discrimination account.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it