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Record W4390884170 · doi:10.1080/13683500.2024.2303625

Authenticity, ethics and restoration of an earthquake-modified landscape: Jiuzhaigou World Natural Heritage Site

2024· article· en· W4390884170 on OpenAlexaff
Cheng Li, Meiyu Wang, Tingting Wang, Yifan Wang, Xue Zhang, Geoffrey Wall

Bibliographic record

VenueCurrent Issues in Tourism · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Waterloo
FundersNational Social Science Fund of ChinaChengdu Science and Technology Bureau
KeywordsTourismNatural heritageWorld heritagePerceptionNatural (archaeology)Natural disasterCultural heritageHeritage tourismEnvironmental ethicsWork (physics)Environmental resource managementNatural landscapeGeographyRestoration ecologyEnvironmental planningSociologyPolitical sciencePsychologyEcologyTourism geographyArchaeologyEngineeringEnvironmental science

Abstract

fetched live from OpenAlex

Restoring a World Natural Heritage Site (WNHS) after a disaster to attract tourists raises multifaceted issues that concern heritage authenticity, environmental ethics, and tourists’ acceptance. In the case of Jiuzhaigou post-earthquake landscape, this study analyses authenticity perceptions, attitudes, and behavioral intentions of tourists and explores environmental ethics through three perspectives. Results reveal tourists’ authenticity perception affects their attitudes and behaviors towards environmental restoration. Moreover, tourists tend to recognize and support Awe for Nature (Awe-N) more than Respect for Nature (Res-N) and significant differentiation exists among potential tourists according to their environmental ethics, demographic characteristics, and behaviors. Altogether, our work presents innovative arguments on post-disaster WNHS restoration by highlighting the complexities of heritage authenticity, environmental ethics, and tourist’ dynamics.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.059
GPT teacher head0.417
Teacher spread0.358 · 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 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

Citations5
Published2024
Admission routes1
Has abstractyes

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