<i>In Anticipation</i> : Educational (Im)mobilities, Structural Disadvantage, and Young People’s Futures
Bibliographic record
Abstract
This paper draws upon qualitative fieldwork undertaken in Hong Kong, over the space of a decade, to reflect upon how educational (im)mobilities are folded into the structures that would seem to determine young people’s anticipation of futures. It draws upon two large research projects in particular – one which involved children’s international migration in search of education; the other which examined examples of young people ‘stuck’ in Hong Kong and prescribed, by virtue of their ‘failure’ in the school system, particular circumscribed life chances. The paper attempts to change the spatial lens through which international education is viewed – away from the focus on ‘exodus’ (Abelmann, N., and Kang, J., 2014. Memoir/manuals of South Korean Pre‐College Study Abroad: Defending Mothers and Humanizing Children. Global Networks 14 (1), 1–22) towards a sense of ‘internalisation’ (and the impact that international education is having ‘at home’). The paper speaks to wider debates concerning educational migrations (the role of students as migrants) and the formative role that education (both domestic and international) plays in contemporary societies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".