Bibliographic record
Abstract
* Preface * Tourism Education in Canada: Past, Present, and Future Directions (Don MacLaurin) * Global Tourism Higher Education--The British Isles Experience (Tom Baum) * Tourism Education in Austria and Switzerland: Past Problems and Future Challenges (Klaus Weiermair and Thomas Bieger) * Tourism and Hospitality Higher Education in Israel (Arie Reichel) * Tourism Higher Education in Turkey (Fevzi Okumus and Ozcan Yagci) * Tourism Higher Education in China: Past and Present, Opportunities and Challenges (Wen Zhang and Xixia Fan) * The Past, Present, and Future of Hospitality and Tourism Higher Education in Hong Kong (Ada Lo) * Tourism and Hospitality Higher Education in Taiwan: Past, Present, and Future (Jeou-Shyan Horng and Ming-Huei Lee) * Travel and Tourism Education in Thailand (Manat Chaisawat) * Past, Present, and Future of Tourism Education: The South Korean Case (Mi-Hea Cho and Soo K. Kang) * Australian Tourism Education: The Quest for Status (Philip L. Pearce) * Index * Reference Notes Included
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.095 | 0.022 |
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".