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Record W2027460090 · doi:10.1108/17554211011037895

Tourism in the Amazon: conclusions and solutions

2010· article· en· W2027460090 on OpenAlexaff
Chandana Jayawardena, Donald Sinclair

Bibliographic record

VenueWorldwide Hospitality and Tourism Themes · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsAmazon rainforestTourismSustainable tourismViewpointsContext (archaeology)HospitalityOriginalitySustainabilityValue (mathematics)EcotourismTourism geographyGeographyMarketingBusinessPolitical scienceRegional scienceSociologyComputer scienceSocial scienceQualitative researchEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.306
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2010
Admission routes1
Has abstractyes

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