The travel behaviour of international students: The relationship between studying abroad and their choice of tourist destinations
Bibliographic record
Abstract
This paper examines why international students opt for their chosen study destination. It also investigates their behaviour as tourists while studying, whether they hosted visits from friends or relatives and their overall economic contribution. The sample consisted of 600 international students studying in higher education institutions in Melbourne, Australia of which 219 responded. A stratified random sampling method was used with the key variables identified as country of origin, gender and university attended. Key questions included: What were the factors that prompted students to study in Australia? How did they become familiar with destinations and tourist attractions during the course of their studies? What tourist attractions and activities were most popular? It was discovered that word-of-mouth was the most significant medium of communication in the selection of educational destination. Most travel undertaken during the period of enrolment was for private purposes. The most popular Melbourne attraction was the Queen Victoria Market and The Great Ocean Road was the most popular attraction statewide. The study also found that tourism related activities undertaken by overseas students contributed approximately A$8.2m to the economy of the state of Victoria. The figure more than doubles to approximately A$17.2m if the expenditures of visiting friends and relatives (VFRs) are included.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".