How can the tourism industry respond to the global challenges arising from climate change and environmental degradation?
Bibliographic record
Abstract
Purpose This paper profiles the WHATT theme issue “How can the tourism industry respond to the global challenges arising from climate change and environmental degradation?” by drawing on reflections from the theme editors and theme issue outcomes, including case study examples from Malaysia, New Zealand and Canada. Design/methodology/approach This paper uses structured questions to enable the theme editors to reflect on the rationale for the theme issue question, the starting point, the selection of the writing team, the material and the editorial process. Findings This paper uses case studies to illustrate how the tourism industry is responding to climate change issues. Additionally, team members of the theme issue from Australia, India, Germany, Malaysia and Canada review some of the latest thinking on the relationships between tourism and climate change. Practical implications This paper outlines challenges and new approaches to the management of climate change in tourism. Originality/value Explores the extent to which innovative approaches, discussed in this theme issue, could be replicated and applied in countries that have yet to take action on tourism-related climate change.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".