Bibliographic record
Abstract
Tourism Policy and Planning: Yesterday, Today, and Tomorrow offers an introduction to the tourism policy process and how policies link to the strategic tourism planning function as well as influence planning at the local, national, and international levels. This fourth edition has been fully revised and updated to reflect the many important developments in the travel and tourism industry and subsequent new policies and present planning process issues in relation to crises – in particular, COVID-19. The fourth edition features: New content on the impact of COVID-19 on tourism policy and planning. New content on the effects of the pandemic on the tourism industry more generally, including topics such as degrowth, common good economy, post-growth economy, tourism lobbies and lobbying, tourism policy/planning and SGDs citizens’ engagement in tourism policy and planning, strategic directions, monitoring, and evaluation of tourism policy. New case studies throughout to illustrate real-life applications of planning and policy at the international, regional, national, and local levels. New case studies across a variety of issues related to flora and fauna, landscapes and geographies, and global destinations such as Ecuador, Canada, New Zealand, the United States, and Belize. New enhanced companion website with chapter assignments and quizzes. Accessible and up to date, Tourism Policy and Planning provides students with an essential introduction to and examination of important policy and planning issues in tourism globally.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.084 | 0.031 |
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".