Global Tourism Higher Education: Past, Present, and Future
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it