Indigenous peoples and tourism: the challenges and opportunities for sustainable tourism
Bibliographic record
Abstract
The Indigenous tourism focus of the 16 papers in this special issue provides readers with an opportunity to explore the dynamics behind an array of issues pertaining to sustainable Indigenous tourism. These papers not only provide a long overdue balance to the far too common, negatively biased media reports about Indigenous peoples and their communities but also highlight the capacity of tourism as an effective tool for realizing sustainable Indigenous development. Throughout the papers reviewed in detail here, readers are reminded of the positive (capacity building) and negative (commodification) realities of Indigenous tourism development. Concomitantly, readers are privy to the practical and theoretical contributions pertaining to the management of cultural values and Indigenous businesses and the social and economic empowerment of Indigenous groups. The main contribution of this special issue, however, is a call for increasing research by, or in collaboration with, Indigenous researchers so that Indigenous authors and editors of academic journals become the norm in academia. Ultimately, Indigenous scholars and tourism providers should be the major contributors to, and commentators about, mainstream and niche approaches to Indigenous tourism management, whilst communities gain visibility not just as the visited “Other”, but as global leaders within tourism and related sectors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".