NEW GLOBAL MARKET AT HIGHER EDUCATION: AN ANALYSIS FOR INTERNATIONAL STUDENT MOBILITY
Bibliographic record
Abstract
The number of international students in undergradute or graduate degrees and researchers have globally risen to 4.2 million in 2013 from 2.1 million in 2000 according to OECD figures. Countries are in a fierce competition in order to attract international students in higher education. However neither state, nor private universities in Turkey have common strategies to attract international students. This study aims to compare statistics of Turkey and other OECD on international student mobility, which has become a global market. Data on this study has been gathered by document analysis. In this study, distribution of international students throughout the world, countries hosting the largest number of international students and tuition fees paid by these students, language of education, international student ratio of Turkey and other OECD countries and budget allocated to higher education in other OECD countries and Turkey have been compared respectively. Students from Turkey and other OECD countries usually prefer countries such as USA, Australia, Canada, Britain, Germany, Switzerland, Finland, Sweden, Netherlands and Spain. The reasons of this choice include alternative language courses provided to international students, discounts on tuition fees, higher chance of having a part time job for the students, importance given to distance learning in these countries, higher budget allocated for research and development and better research and development facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".