Policy mobilities in the race for talent: competitive state strategies in international student mobility
Bibliographic record
Abstract
Abstract In the first decade of the 21st century, several countries introduced a series of strikingly similar international student mobility policies and initiatives. Driven by a desire to expand their international student market share and to benefit from the potential contributions that international students can make to national innovation agendas, comparable policy tools were introduced in multiple states across the fields of international trade, higher education and immigration. This paper challenges depictions of these changes as a natural evolution of economic globalisation and draws on the policy mobility literature to interrogate the why and the how of the policymaking process. Drawing on research with policymakers, the paper comparatively examines the introduction of international student policies and initiatives in Canada and the UK from 2000 to 2010, and illustrates that the policy development path is the result of a competitive process wherein certain policy ideas become popular and travel, or become mobile. In so doing, I draw attention to the relationship between international student mobility, changing geographies of higher education and global knowledge economy discourses, highlighting the interconnected nature of the policy sphere as competitor jurisdictions seek to outdo each other in their attempt to attract and retain international students.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".