Classifications and clarifications: rethinking international student mobility and the voluntariness of migration
Bibliographic record
Abstract
Linking insights from the fields of international education and migration/mobilities studies – in particular, those offered by Streitwieser [(2019). ‘International Education for Enlightenment, for Opportunity and for Survival: Where Students, Migrants and Refugees Diverge’. Journal of Comparative & International Higher Education 11: 4–9] and Bivand Erdal and Oeppen [(2018). ‘Forced to Leave? The Discursive and Analytical Significance of Describing Migration as Forced and Voluntary’. Journal of Ethnic and Migration Studies 44 (6): 981–998] – we introduce a new approach to analysing international student mobility (ISM) as higher education, migration, and mobility intertwine in increasingly complex ways. First, we attend to the messiness of ISM’s terms, data, and practices, offering clarifications of some commonly-used terms and considerations for stakeholders. We then present our updated conceptual lens which positions ISM as a landscape structured by the interface of two continuums: (1) the discretion to move, and (2) opportunity. By better reflecting the spectrum of ISM’s voluntariness and its impact on opportunity, we highlight the ongoing reproduction, amplification, dissolvement, and restructuring of privilege within international education. Our approach also visibilises students from displaced, refugee, and forced-migrant backgrounds. Ultimately, we problematise the loose subfield of ISM and stress the need for increased interdisciplinary engagement.
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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 | 0.034 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.023 | 0.041 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".