Selective Migration Policy Models and Changing Realities of Implementation
Bibliographic record
Abstract
Abstract Selective migration policies are proliferating worldwide as governments try to attract scientists, highly skilled engineers, medical professionals and information technology professionals. Selective migration policies can be grouped into three ideal‐typical models: the Canadian “human capital” model based on state selection of permanent immigrants using a point system; the Australian “neo‐corporatist” model based on state selection using a point system with extensive business and labour participation; and the market‐oriented, demand‐driven model based primarily on employer selection of migrants, as practised by the US . After providing an overview of each model, the article compares the three models in terms of policy outcomes as measured by various metrics and then explains how Canadian, Australian, and US governments have recently adopted policies from one another and deviated from their respective selective migration policy models. Policy Implications Canadian and Australian governments select immigrants using point systems but diverged in 1996 on human capital criteria of higher education and general experience U.S. employers select economic migrants and majority initially come on temporary visas More highly‐skilled foreigners go to the U.S. than to Canada, Australia and other countries using point systems combined. Canadian and Australian governments shifting policies toward the U.S. demand‐driven model, with increasing preference given to employer‐sponsored immigrants and those already working on temporary visas. Canadian government shifting point system criteria from human capital toward specific occupations and may abandon point system altogether.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".