International Student Migration: Mapping the Field and New Research Agendas
Bibliographic record
Abstract
ABSTRACT Despite rapid growth in the student component of global migration flows, the study of international student migration/mobility (ISM) is a relatively neglected field in migration research. This special issue helps to address this lacuna. This introductory paper highlights the contradictions between international students as ‘desired’ because of their internationalism and fee contributions, and as ‘unwanted’ because of the politics of migration control especially in the context of the securitisation of study in the post 9/11 scenario. It argues that interrogating the terms ‘international’ and ‘students’ is critical to addressing the slipperiness that underlies these contradictions. Focusing on students per se ignores their multiple roles, as family members, actual or potential workers, or perhaps refugees and asylum‐seekers, while definitions of international students ignore the diversity of study that students undertake. After summarising the papers that follow, this paper concludes with an agenda for future research on ISM: greater theoretical insight drawing on the cognate field of mobility studies; more in‐depth ethnographic research on mobile students who recognise their multiple roles in knowledge diffusion and social reproduction; further research on ISM datasets and quantitative surveys, which employs statistical analysis; more attention paid to gender and race as they relate to ISM; and a stronger link to pedagogy and systems of higher education and knowledge production. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.028 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".