Recruitment and Retention of Immigrants in a Global Labour Market: Implications for Policy
Bibliographic record
Abstract
Bio: Chris Robinson studied economics at the London School of Economics and the University of Chicago, and has been a faculty member at the University of Western Ontario since 1977. His research has focused on human capital and wage issues including human capital specificity, labour supply, migration, and unions and he has published a wide range of articles on these topics in scholarly journals. From 1993 to 2003 he served as associate editor of the Journal of Labor Economics. From 2001 to 2010 he held the CIBC Chair in Human Capital and Productivity at the University of Western Ontario and was responsible for the overall direction of the CIBC Project in Human Capital and Productivity and CIBC Centre for Human Capital and Productivity. In 2010 he was awarded the H. Gregg Lewis Prize for the best paper published in the Journal of Labor Economics for 2008 and 2009.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".