Students on the move? Intellectual migration and international student mobility
Bibliographic record
Abstract
International student mobility, taking place within the framework of globalisation, internationalisation and transnationalism, has attained much attention. This paper adopts the Intellectual Migration framework to further our understanding of mobility regarding international higher education. It simultaneously studies China-born students in both China and North America to empirically examine the propensity for student mobility across national borders and the determining factors behind the realisation of such mobility under the same set of geopolitical and international circumstances. The analysis is based on a set of cross-sectional surveys conducted in the 2017–2019 period that yields over 1600 data points. We compare the ‘who’, ‘why’ and ‘where’ aspects of migration between domestic students in China and Chinese international students in North America to delineate the factors underlying international student mobility. By highlighting aspirations and capabilities on mobility outcomes, this paper contributes to differentiating mobility between undergraduate and graduate students and the implications for social inequality. Our analysis also reveals the unequal spatial distributions of educational resources between intellectual gateways and peripheries, and by extension between the Global North and the Global South. The findings of this paper have policy implications on improving the quality, accessibility, and equity of higher education.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".