Identifying the Impact of Stringent Immigration Rules on International Students: The Case of Türkiye
Bibliographic record
Abstract
Higher education is one area that has seen the most impact of globalisation, and the free exchange of scholarships is noted to be on the rise. By taking the course of other leading nations in higher education, such as the US and Canada, Türkiye, through the Council of Higher Education (YÖK), has since the early 2010s adopted a policy to increase international student enrolment in its universities. However, recent changes in the immigration laws by the Department for Immigration Management (Göç İdaresi) are noted to portend to thwart the efforts of YÖK in what could be said to be a case of conflicting roles between two state institutions. This study presents an analysis of the impacts of these stringent rules on international students by analysing survey results conducted on international students in Türkiye. The work contributes to both policy and scholarship. Its contribution to policy can be seen in its attempt to highlight the intricacies of finding a balance between national security concerns and the need to foster academic exchange and global engagement in higher education. The study also contributes to the general discipline of migration studies in addition to the field of urban and regional planning with direct contributions to areas such as global competitiveness of cities, urban growth dynamics, and cultural diversity and social integration of urban areas. This is made possible by considering the impacts of the influx of international students into cities and the challenges they face, which this study outlines. Key among the findings include the likelihood of persons who have reported having issues with Göç İdaresi not recommending Turkiye to others as a higher education destination. Thus, this demonstrates a case where immigration rules are having a counterproductive effect on the efforts put in by higher education policymakers.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".