Bibliographic record
Abstract
In the context of wider debates around students’ international mobility, this chapter discusses the findings of several recent studies examining the motivations and experiences of international students from the UK. As we have already noted, the majority of research papers published to date have focussed on individuals moving from countries in East and Southeast Asia to the major student-receiving destinations in the US, Canada, Australia, New Zealand or the UK (e.g. Butcher, 2004; Collins, 2006; Ong, 1999; Waters, 2008). The popularity of Anglophone destinations amongst the wider international student population reflects in large part the high value attached to an English-medium, ‘Western’ education — English is widely thought of as the language of international business and trade (Ong, 1999). The motivations and experiences of (Englishspeaking) students from the UK, seeking an international education, have until recently therefore been unclear. Furthermore, universities in the UK consistently top international league-tables and rankings (for example, the annual Times Higher Education World University Rankings), whilst Britain is second only to the United States as the most significant importer of international students worldwide (British Council, 2004). This begs the question: Why , then, do UK students choose to study abroad? These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".