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Record W2125661389 · doi:10.5539/jsd.v7n1p17

Regional Tourism at the Cross-Roads: Perspectives of Caribbean Tourism Organization’s Stakeholders

2013· article· en· W2125661389 on OpenAlexvenueno aff
Berneece S. Herbert, Colmore S. Christian

Bibliographic record

VenueJournal of Sustainable Development · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
FundersNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsTourismZoningSustainable tourismPoliticsStakeholderTourism geographyEcotourismBusinessCaribbean regionNatural resourceSmall Island Developing StatesGeographyEconomic growthEconomyEnvironmental resource managementPolitical scienceClimate changeEconomicsEcologyPublic relations

Abstract

fetched live from OpenAlex

The Caribbean has experienced considerable fluctuations with many of the small island-nations of the Region being highly vulnerable to socio-political, environmental and economic changes. The Caribbean Tourism Association (CTO) contends that this Region is highly dependent on tourism, possibly more than any other region in the world, but globalization has left the countries of the Region with limited economic alternatives. The result is that tourism has emerged as the largest employer and the foremost foreign exchange earner in the Region. This survey research study, data from which were analyzed with SPSS for Windows, explored three research questions and identified the Region’s strengths, assets and issues as perceived by stakeholders at the CTO’s 2010 Sustainable Tourism Conference. People, culture and favorable weather were identified as strengths. However, critical issues such the absence of clear political and policy directions, loss of biodiversity and natural resources, need for more efficient zoning and land use planning for integrated tourism development, and insufficient stakeholder involvement were characterized as challenges that must be addressed. Recommendations are proposed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.033
GPT teacher head0.296
Teacher spread0.263 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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