Tourism in the Amazon: conclusions and solutions
Bibliographic record
Abstract
Purpose The paper aims to present answers to the strategic question: “Does sustainable tourism offer solutions for the protection of the Amazon rainforest?” It also aims to capture the essence of conclusions of eight papers written by 11 tourism experts to the Worldwide Hospitality and Tourism Themes (WHATT) issue on tourism in the Amazon, South America. Design/methodology/approach The paper integrates all solutions suggested in these eight papers and sought to provide a succinct response to the strategic question. Findings While providing a helicopter view of the key challenges of sustainable tourism development in the Amazon, this paper proposes implementable solutions to those challenges. Using the 2009 WHATT roundtable discussion in Brasilia as the foundation, this paper addresses some of the most significant issues affecting the Amazon and its people in the context of tourism in Bolivia, Brazil, Colombia, Ecuador, Guyana, Peru, and Suriname. In conclusion, 12 key suggestions are made for the Amazon, which envisions healthy and sustainable growth of tourism for its people. Originality/value This paper provides fresh perspectives on many relevant issues by analysing inputs, viewpoints, comments, and suggestions of many regional tourism experts. Readers with interests in the Amazon or similar tourism destinations around the world would benefit from this paper.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".