Contributor biographies
Bibliographic record
Abstract
He is an international expert on the computable general equilibrium modelling of tourism and related policies, for example modelling the effects of foot and mouth disease on tourism in the UK and September 11 on tourism in the USA.The results from his research on the effects of FMD on tourism were cited by the Minister of Tourism and in the House of Lords.He has published in a range of economics and tourism journals and has also written research reports for numerous governmental bodies in the UK as well as in other countries.Adrian O. Bull is Associate Professor of Tourism at the University of Lincoln in England.Previously he had experience in both tour operation and the hospitality industry, and taught at Southern Cross University in NSW, Australia.He completed his PhD (on hedonic pricing in hotel markets) in 1998 at Griffith University in Brisbane.He is the author of the best-selling international textbook The Economics of Travel and Tourism (Longman, 1995), and has researched and published in a number of tourism and hospitality-related areas, relating to markets and pricing, ocean and coastal tourism, impacts and management.His current interests include studies of market definition in tourism, strategies for overcoming seasonality issues in coastal tourism, and the integration of tourism variables into bioeconomic ocean modelling.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.350 | 0.195 |
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".