Tourism in the Amazon: identifying challenges and finding solutions
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide an overview of the tourism sector in the Amazon regions of Bolivia, Brazil, Colombia, Ecuador, Guyana, Peru, and Suriname and then discuss the manner in which tourism activity assists the protection of the Amazon rainforests. The paper also describes the manner in which the first World Hospitality and Tourism Themes Roundtable on Tourism in the Amazon is organized in 2009. Design/methodology/approach Teams of researchers from the Ministries of Tourism, the private sector and academia in the Member Countries of Treaty for Amazon Cooperation collaborated to address, in ten‐page papers, the question “Does sustainable tourism offer solutions for the protection of the Amazon rainforest?” Findings The paper provides valuable information on the current state of tourism policy and practice in the Amazon Member Countries. It also articulates the challenges that attend the development of sustainable tourism as a mechanism for the protection of the Amazon. Practical implications Tourism policy officials and managers, should benefit from the discussions of the prospects and challenges that attend the practice of sustainable tourism in the Amazon region. They will also find interesting guidelines and recommendations for action based upon the many destinations and tourism regions under examination. Originality/value The issue of sustainable tourism and the rainforest is very topical and this paper will be of immense value to scholars, researchers and tourism practitioners.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".