Bibliographic record
Abstract
1. Sport Tourism: An Introduction and Overview Brent W. Ritchie & Daryl Adair 2. Secular Pilgrimage and Sport Tourism Sean Gammon (University of Luton) 3. Where the Games never cease: The Olympic Museum in Lausanne, Switzerland Daryl Adair 4 . Winter Sport Tourism in North America Simon Hudson (University of Calgary) 5. Adventure Sport and Tourism in the French Mountains Phillipe Bordeau (Institute de Geographie Alpine, France) Jean Cornelop (Universite Blaise Pascal) & Pascal Mao (CERMOSEM) 6.More Than Just a Game: The Consequences of Golf Tourism Catherine Palmer (University of Brighton) 7. Exploring Small- Scale Sport Event Tourism 12 Competition Brent W. Ritchie 8. Host Community Reactions to Motorsport Events Liz Fredline (Griffith University) 9. Crime and Sport Events Tourism Michael Barker (Heilongjiang University, China) 10. Sports Tourism and Urban Regeneration C. Michael Hall (University of Otago) 11.Sport Tourism in Crisis: Exploring the Impact of the Foot and Mouth Crisis on Sport Tourism in the United Kingdom Graham A. Miller ( University of Westminster) & Brent W. Ritchie 12. Beyond Impact: A General Model for Sport Event Leverage Laurence Chalip (University of Texas at Austin) 13.Sports Tourism in the United Kingdom John Deane ( University College Worcester) & Michelle Callanan (Birmingham College of Food, Tourism and Creative Studies) 14. The Future of Sport Tourism Joseph Kurtzman & John Zauhar (Sport Tourism International Council, Ottawa) 15. Conclusions and Reflections Brent W. Ritchie & Daryl Adair.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".