Bibliographic record
Abstract
Tourism and Indigenous Peoples is a unique text examining the role of indigenous societies in tourism and how they interact within the tourism nexus. Unlike other publications, this text focuses on the active role that indigenous peoples take in the industry, and uses international case studies and experiences to provide a global context to illustrate best practice and aid comparison. First published over ten years ago the editors, Butler and Hinch, have thoroughly revised and updated the text to bring together a new collection of contributions and case studies from recognised international authors and those with first hand experiences in this area. Divided into five main sections, the text looks at this topic under the following headings: Involvement: Uses case studies to discuss and compare such as 'campfire' programmes in east Africa, and the employment of indigenous peoples as guides, amongst other cases, Turbulence: Host guest relationships, conflicts on communities and contrasting strategies and results of tourism in indigenous villages in South Africa. Issues: Discusses issues such as authenticity, religious beliefs and managing indigenous tourism in a fragile environment. Progress: Looks at tourism education, tourism and cultural survival and examples of the policy and practice of indigenous tourism. Conclusions: Five contributions from indigenous people on North America, Australasia and Europe to discuss implications and experiences. Each section uses international case studies from, for example, Australia, New Zealand, Nepal, Namibia, Thailand, Saudi Arabia and South America.
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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 | 0.003 | 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 | 0.008 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".