A Competitive Analysis of International Students Attraction Practices in Georgia and World Leading Students’ Destination Countries (Benchmark of the Best Cases)
Bibliographic record
Abstract
Student worldwide mobility has increasingly become an important indicator for the degree of internationalization and reputation of higher education institutions. The universities focus on the attraction of international students as a potential source of financial income and prestige of their educational provisions. Massive global competition between countries regarding attraction of oversea students have been fluctuating during decades. Nowadays the US, UK, Canada, Germany (the top recruiter countries) have been caught up and even overtaken by China, India, Malaysia. The central question of discussion is how the sender countries (China, Turkey) managed to become receivers of international students and what are the main issues small countries, like Georgia, can benchmark from them. Moreover, the US and UK have accumulated the best experiences that might be shared with the rest of the world. As international student mobility has become intensive and economically beneficial for the Georgian higher education institutions the main goal of the study is to examine all those trends and marketing strategies that are popular and rational for the Georgian context. The core aim of the research is to identify gaps in local context and offer further development opportunities for the Georgian universities in terms of increasing international students’ recruitment in Georgia and transforming the small country into the ‘regional educational hub’.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".