Emergence of Central and Eastern European Countries as Destinations of International Education
Bibliographic record
Abstract
The dimension of international student mobility has evolved with regard to its direction, flow, and magnitude. The rise in internationally mobile students across the world point to how scholar mobility remains at the heart of international higher education. Historically speaking, the movement has largely been from the global south to the global north. The countries in the north have time and again launched initiatives to make their higher education institutions attractive to international students. International students, especially from China and India, have preferred the US, UK, Australia, Canada, and some parts of Europe like Germany. Europe, which along with the US, has been broadly construed as the “west”, especially in the eyes of the developing world, has also made several organized efforts to develop as a higher education destination. Erasmus Programmes, the Bologna Process, and other policy instruments and architecture have provided Europe with an edge over others in making a place for themselves in the higher education landscape. The very conventional movement from the developing world to developed nations has invited deliberations on the hierarchies that exist in international student mobility. This chapter sheds light on the changing student preferences with regard to higher education, followed by an exploration of student mobility trends in Europe, and focuses on how central and eastern European countries (CEEC), with Latvia being a case in point, have come to be an attractive destination for international students, especially students from India. The chapter analyses these trends in students’ mobility toward CEEC by unearthing the reasons for this trend and estimating the changes in this in times of war and political changes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".