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Record W2990785303 · doi:10.1080/09669582.2019.1694526

Collaboration gaps and regional tourism networks in rural coastal communities

2019· article· en· W2990785303 on OpenAlexaffabout
Mark C. J. Stoddart, Gary Catano, Howard Ramos, Kelly Vodden, Brennan Lowery, Leanna Butters

Bibliographic record

VenueJournal of Sustainable Tourism · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsDalhousie UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsTourismWork (physics)Social network analysisPublic relationsDestinationsMeaning (existential)SociologyMarketingBusinessRegional scienceKnowledge managementPolitical scienceEngineeringSocial capitalPsychologySocial scienceComputer science

Abstract

fetched live from OpenAlex

Tapping into and creating broad networks is integral to connecting communities and destinations to wider flows of tourists and ensuring local benefits from tourism development. However, little research has probed how communities build these connections. This article examines how tourism stakeholders perceive and practice the work of network-building and assess the challenges they face in pursuing this work in regional tourism development. Drawing on survey and focus group data from Atlantic Canada, we identify “collaboration gaps” between the perceived value of network-building and related social practices. Social practice theory is used to analyse tourism network-building and explain why collaboration gaps exist and persist. Our analysis found three gaps: between meaning and practice; vertical collaboration gaps related to the scale of network-building; and horizontal collaboration gaps related to the range of actors involved in tourism networks. These collaboration gaps can be addressed through a focus on meaning, competencies, and materials as means to foster successful collaborations and overcome gaps.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.287
Teacher spread0.276 · 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 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

Citations39
Published2019
Admission routes2
Has abstractyes

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