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Record W2555325775 · doi:10.1108/whatt-07-2016-0039

How can the tourism industry respond to the global challenges arising from climate change and environmental degradation?

2016· article· en· W2555325775 on OpenAlexaboutno aff
Vik­neswaran Nair, Badaruddin Mohamed, Toney K. Thomas, Richard Teare

Bibliographic record

VenueWorldwide Hospitality and Tourism Themes · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTheme (computing)TourismClimate changeOriginalityValue (mathematics)SociologyPolitical scienceEnvironmental ethicsPublic relationsEnvironmental resource managementSocial scienceEconomicsComputer scienceEcologyQualitative researchLaw

Abstract

fetched live from OpenAlex

Purpose This paper profiles the WHATT theme issue “How can the tourism industry respond to the global challenges arising from climate change and environmental degradation?” by drawing on reflections from the theme editors and theme issue outcomes, including case study examples from Malaysia, New Zealand and Canada. Design/methodology/approach This paper uses structured questions to enable the theme editors to reflect on the rationale for the theme issue question, the starting point, the selection of the writing team, the material and the editorial process. Findings This paper uses case studies to illustrate how the tourism industry is responding to climate change issues. Additionally, team members of the theme issue from Australia, India, Germany, Malaysia and Canada review some of the latest thinking on the relationships between tourism and climate change. Practical implications This paper outlines challenges and new approaches to the management of climate change in tourism. Originality/value Explores the extent to which innovative approaches, discussed in this theme issue, could be replicated and applied in countries that have yet to take action on tourism-related climate change.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.733
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.041
GPT teacher head0.285
Teacher spread0.243 · 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 designObservational
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

Citations2
Published2016
Admission routes1
Has abstractyes

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