Southern Europe perspectives on international student mobility
Bibliographic record
Abstract
During the last four decades, higher education institutions (hereafter, HEIs) have experienced an unprecedented level of internationalization, closely linked to pressures induced by economic globalization (Kehm and Teichler 2007). The dominance of post-industrial capitalism, a revolution brought about by new information technologies and the postcolonial scenario of emerging countries demanding access to higher education are at the core of a worldwide engagement with internationalization (Lumby and Foskett 2016). The demand for status-generating tertiary education from middle class and elite families in countries such as China, India, South Korea, Brazil and Nigeria has stimulated the struggle between nations that seek to dominate the global education market (Waters and Leung 2013). The most prominent universities in the United States, United Kingdom, Australia, Canada and Germany have begun offering distance education courses, joint programmes and academic partnerships, opening campus branches overseas and, of course, recruiting as many mobile students as possible (Walker 2014). In this sense, internationalization might rather be labelled ‘transnationalization’ as its principal feature is not the expansion of HEIs on an international scale but rather the commercialization of educational goods and services worldwide (Verger et al. 2016). In fact, educational goods are now included in the General Agreement on Trade in Services (GATS) of the World Trade Organization (WTO).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".