Bibliographic record
Abstract
Executive Summary This article examines China’s recruitment of high-skilled migrants within a broader comparative analysis of selective migration strategies and adds the neglected Chinese case to the comparative immigration policy literature. China adopted certain selective migration polices from other countries, such as Canada and the United States, but its selective migration strategy does not fit established categories. Beginning in 2013, the Chinese communist government enacted new laws and issued new regulations to attract foreign talent and better regulate the entry, residence and employment of foreigners. The contours of China’s distinctive selective migration policy became more defined as Beijing established a point system combined with a job offer requirement; reformed the Z visa, which is issued to those with a college degree who intend to work in China; established the R visa for exceptionally high-skilled migrants; and adopted more liberal policies on permanent residency for the highly skilled. These policies, combined with the opportunities presented by China’s rapidly growing economy, attracted growing numbers of university graduates and propelled China, to become, for a brief period before the COVID-19 pandemic, a major destination for high-skilled migrants.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".