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Record W754917385 · doi:10.1177/0047287515592972

Residents’ Support for Tourism

2015· article· en· W754917385 on OpenAlexaboutno aff
Robin Nunkoo, Kevin Kam Fung So

Bibliographic record

VenueJournal of Travel Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismSocial exchange theoryPerceptionSet (abstract data type)Government (linguistics)Baseline (sea)MarketingStructural equation modelingPsychologySocial psychologyBusinessGeographyPolitical scienceComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Social exchange theory (SET) has made significant contributions to research on residents’ support for tourism. Nevertheless, studies are based on an incomplete set of variables and are characterized by alternative, yet contradictory, and theoretically sound research propositions. Using key constructs of SET, this study develops a baseline model of residents’ support and compares it with four competing models. Each model contains the terms of the baseline model and additional relationships reflecting alternative theoretical possibilities. The models were tested using data collected from residents of Niagara Region, Canada. Results indicated that in the best fitted model, residents’ support for tourism was influenced by their perceptions of positive impacts. Residents’ power and their trust in government significantly predicted their life satisfaction and their perceptions of positive impacts. Personal benefits from tourism significantly influenced residents’ perceptions of the positive and negative impacts of tourism. The study provides valuable and clearer insights on relationships among SET variables.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.302
GPT teacher head0.503
Teacher spread0.201 · 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 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

Citations344
Published2015
Admission routes1
Has abstractyes

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