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Record W2067429601 · doi:10.1108/17506181211233036

Island tourism: destinations: an editorial introduction to the special issue

2012· article· en· W2067429601 on OpenAlexaff
Jenny Cave, Keith G. Brown

Bibliographic record

VenueInternational Journal of Culture Tourism and Hospitality Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsCape Breton University
Fundersnot available
KeywordsTourismDestinationsOriginalityField (mathematics)Value (mathematics)Destination managementRegional scienceHospitality management studiesMacroService (business)SociologyMarketingKnowledge managementPublic relationsGeographyPolitical scienceBusinessComputer scienceQualitative researchSocial science

Abstract

fetched live from OpenAlex

Purpose This editorial aims to situate the papers chosen for this special issue within academic literature and identify their contributions to new knowledge. Design/methodology/approach The editorial first discusses tourism research literature pertinent to the idiosyncrasies of destination management in island contexts. Second, the paper identifies the contributions made to this field by the authors and the implications of their innovative research for island tourism and destination management. Findings Each paper contributes, in its way, to the field of island tourism, either by integration of explorations of theory, shifting paradigms or revealing new knowledge. This special issue contains two seminal papers by top academic leaders of the fields of islandness and HRM in island destinations. It also presents papers that comment on destination management issues at macro and micro levels. Originality/value Collectively this collection of papers offers new perspectives concerning the challenges of creating destination image in peripheral locations, the impacts of global mobilities (inward and outward) on destination labor markets, models for sustainable destination development, the welcome extended to visitors and returning locals by island communities, destination positioning strategies and service interactions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.367
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.388
Teacher spread0.360 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations25
Published2012
Admission routes1
Has abstractyes

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