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Record W3216729019 · doi:10.24043/isj.183

Sustainable tourism and the Sustainable Development Goals in sub-national island jurisdictions: The case of Tobago

2021· article· en· W3216729019 on OpenAlexvenueno aff
Preeya Mohan

Bibliographic record

VenueIsland Studies Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable tourismTourismSustainable developmentStakeholderBusinessEcotourismEnvironmental planningTourism geographyEnvironmental resource managementSustainabilityPolitical sciencePublic relationsGeographyEconomics

Abstract

fetched live from OpenAlex

Tourism has the potential to contribute to achieving the Sustainable Development Goals (SDGs) agreed to by United Nations member states. For sustainable tourism to be successful, stakeholders must be involved in the process. The aim of this study is to consider the extent to which sustainable tourism contributes to achieving the SDGs and how tourism stakeholders understand and implement sustainable tourism. Specifically, the study adopted a qualitative approach and used the case study of Tobago. The data were collected using focus groups of tourism stakeholders. The research revealed that stakeholders embraced the SDGs despite a lack of understanding. They were unable to provide a comprehensive definition of sustainable tourism and their relation to the SDGs, but recognised its traditional components along with specific island features. Stakeholders more easily listed sustainable tourism practices and potential and their link to the SDGs. The barriers to sustainable tourism centred mainly on the role of the local governing body and political affiliation, dependency on the mainland, and prohibitive costs. Action is needed to facilitate broader stakeholder awareness and collaboration in support of efforts to enhance sustainable tourism and the achievement of the SDGs, where policymakers need to act as a catalyst for 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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.026
GPT teacher head0.344
Teacher spread0.317 · 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 designQualitative
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

Citations18
Published2021
Admission routes1
Has abstractyes

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