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Record W2305026327 · doi:10.1177/1467358415583738

Stakeholder collaboration: A means to the success of rural tourism destinations? A critical evaluation of the existence of stakeholder collaboration within the Mournes, Northern Ireland

2016· article· en· W2305026327 on OpenAlexaff
Emma J McComb, Stephen Boyd, Karla Boluk

Bibliographic record

VenueTourism and Hospitality Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStakeholderTourismBusinessDestinationsStakeholder analysisContext (archaeology)Rural tourismMarketingPublic relationsTourism geographyPolitical scienceGeography

Abstract

fetched live from OpenAlex

Tourism has long been pursued by governments as a means of development in rural areas. Negatively, rural areas have certain characteristics that inhibit their ability to achieve the full benefits of tourism. Consequently, many rural tourism destinations to date have found that the benefits to be gained are over-stated. Stakeholder collaboration has been deemed critical for the success of sustainable tourism. In fact, in the context of rural tourism destinations stakeholder collaboration can be particularly advantageous in addressing specific factors relevant to rural tourism destinations that may inhibit the success of the destination. However, successful attempts to implement stakeholder collaboration have been limited. A growing body of literature reveals that successful stakeholder collaboration relies on numerous elements, which have to be incorporated for the success of the process. The paper reveals how simply attempting to implement stakeholder collaboration is not enough for its success, instead various components need to be incorporated throughout the continuous process, in particular attention is paid to establishing trust across the various stakeholder group.

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)0.016
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science 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 score0.308
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.150
GPT teacher head0.419
Teacher spread0.268 · 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

Citations107
Published2016
Admission routes1
Has abstractyes

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