Recruiting High Skill Labour in North America: Policies, Outcomes and Futures
Bibliographic record
Abstract
Abstract This article compares Canadian and US recruitment of highly skilled workers, defined by educational, skill, and occupational criteria. Analysis shows that Canada disproportionally recruits skilled workers as legal permanent residents whereas family reunification dominates in the US. But such contrast ignores the large temporary skilled worker flows to the US and the on‐going reliance on them, or the growing use of temporary labour in Canada, including skilled workers. Data is presented on the admission of skilled migrants; recent and future policy developments are discussed. Comprehensive immigration reform is back on the US agenda with potential to increase the migration of skilled immigrants, to utilize a point system for some, and to continue the role of employers in the H1B visa programme. Canada has recently moved to a model of high skill labour recruitment that is characterized by decentralized selection mechanisms, and is demand driven and employer instigated. Policy Implications In studies of high skill international worker flows, it is insufficient to focus only on permanent resident policies; temporary worker programmes also offer entry to high skilled workers. Although skill can be defined by high education and professional or science based occupation, some countries seek skilled workers in the trades. New Canadian policy includes a Skilled Trades class. In the United States, the congressional system often produces incremental change on aspects of skilled worker policy. In Canada, the consolidation of power in the parliamentary executive is allowing substantial change in how skilled international workers will be recruited.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".