Movement from emerging economies to small island states: motivations of Nigerian educational tourists in North Cyprus
Bibliographic record
Abstract
Although core countries, in particular the UK, USA, Canada, and Australia, have been the most popular destinations for educational tourism, a substantial number of educational tourists have recently been travelling to other European, Asian, and African countries. There has been significant research into student mobility from developing countries to core countries, but research on student mobility between developing countries—in particular the movement from emerging economies to small island states—is scarce. North Cyprus has recently become a thriving international educational tourism destination. This study explores the factors affecting educational tourists’ decision-making, with a specific focus on Nigerian students, about whom very little is known regarding their decision to study abroad and their choices of host country and host university. Data from a qualitative study carried out in the small island state of North Cyprus is used to examine the factors that push Nigerian educational tourists away from their home country to seek tertiary education opportunities elsewhere as well as the factors affecting their host country and host institution choices. Three main themes emerged from the data analysis, namely: Studying Overseas, Country Choice, and Host Institution. The results of this study are useful for policymakers in small island states, who wish to establish or improve an educational tourism industry, as well as for university decision-makers who wish to increase their institutions’ success at attracting foreign students.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".