Policy options for managing international student migration: The sending country's perspective
Bibliographic record
Abstract
A consequence of the dramatic rise in international student mobility is the trend for international students to remain in the country in which they study after graduation. Countries such as Australia, the UK and Canada stand to benefit from international student migration, as they are able to fill skill shortages with locally trained foreign students who also expand the demand for goods and services and add to gross national production. The effects on the sending country, however, are potentially less favourable and the emigration of highly educated people can have a detrimental effect, depleting an already scarce resource. However, more recently it has been suggested that an increasing proportion of migratory movement is temporary and that sending countries may benefit from circular or temporary migration via financial remittances, technology transfer, entrepreneurial partnering, and the development of personal networks and diplomatic ties. This paper will consider the impacts of international student migration on sending countries and discuss the policy responses that various sending countries have employed in attempt to regulate student migration.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".