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Record W4413089376 · doi:10.1002/jtr.70087

Analyzing Tourist Online Environmental Discourse With Big Data: A Study of 43 UNESCO Natural World Heritage Sites

2025· article· en· W4413089376 on OpenAlexaboutno aff
Salman Yousaf, Jong Min Kim

Bibliographic record

VenueInternational Journal of Tourism Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsTourismSustainabilityWorld heritageNatural heritageSustainable tourismNatural (archaeology)Norm (philosophy)Heritage tourismGeographyPolitical scienceTourism geographyEcologyArchaeology

Abstract

fetched live from OpenAlex

ABSTRACT This study evaluates the impact of tourists' online environmental discourse (OED) on satisfaction with UNESCO Natural World Heritage (NWH) sites, applying norm activation theory and theories of reasoned action and planned behavior. It addresses sustainability concerns of NWH sites impacted by tourism, analyzing 130,264 TripAdvisor.com reviews covering 43 NWH sites in the United States, United Kingdom, Australia, New Zealand and Canada. The findings show that environmental content in reviews relates to higher ratings, indicating a positive influence of OED on tourists' satisfaction with NWH sites. This study makes significant contributions to the emerging research on NWH sites and scholarly discourse on sustainable tourist behaviors. The implications of these findings are pivotal for shaping effective management strategies for NWH sites.

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)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.440
Teacher spread0.356 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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