Bibliographic record
Abstract
Visiting friends and relatives (VFR) travel is a substantial segment of tourism globally. In many countries, VFR travel represents a large proportion of visitor movement. The size of the segment is often underestimated because official data only reveal VFR by purpose of visit or VFR by accommodation, contributing to the underestimation of the size of VFR travel. Similarly, there is a lack of research that considers the role of the VFR host in VFR travel which results in a lack of understanding. Clearly, the role of the host is critical in VFR travel and it is what centrally defines VFR. This study contributes to the research in VFR travel through providing research related to hosting VFRs. Of note, this study was undertaken in Turkey, which makes a significant contribution to scholarship given the lack of research that has been undertaken outside of Australia, New Zealand, Canada, the United Kingdom and the United States, which are the areas in which VFR travel research has dominated. This study determined the profiles and characteristics of 423 VFR travellers to Nevsehir, Turkey, and their hosts. Accordingly, this study provides a significant contribution to the scholarship of tourism by providing rich data on an area of tourism (hosting VFRs) that had to date, been overlooked.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".