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Record W2164883534 · doi:10.1108/17506181011045163

Island tourism: marketing culture and heritage – editorial introduction to the special issue

2010· article· en· W2164883534 on OpenAlexaff
Keith G. Brown, Jenny Cave

Bibliographic record

VenueInternational Journal of Culture Tourism and Hospitality Research · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsCape Breton University
Fundersnot available
KeywordsTourismCultural heritageMarketingGeographyPolitical scienceBusinessArchaeology

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 marketing of cultural and heritage tourism products at island destinations around the globe. Second, the contributions made to this field by the authors in this volume and their implications for theory, industry dynamics and tourism product as well as to island communities are identified. Findings Each paper contributes to the field, either by explorations of theory, shifts in paradigm or by revealing new knowledge. Originality/value Collectively this collection of papers offers new perspectives on the special characteristics of island tourism, community dynamics, the role of marketing and the development of sustainable cultural and heritage tourism products in island contexts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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: Editorial
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0070.003
Open science0.0010.001
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0110.003

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 head0.011
GPT teacher head0.339
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), 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

Citations37
Published2010
Admission routes1
Has abstractyes

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