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Record W4402460286 · doi:10.1080/1743873x.2024.2398101

Resident well-being and perceptions of World Heritage Site management: divide between urban and rural WHSs

2024· article· en· W4402460286 on OpenAlexaff
Sina Kuzuoglu, Selenay Ata, Bengi Ertuna, Burçin Hatipoğlu

Bibliographic record

VenueJournal of Heritage Tourism · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWorld heritageGeographyPerceptionEnvironmental planningEnvironmental resource managementArchaeologyTourismPsychologyEnvironmental science

Abstract

fetched live from OpenAlex

This comparative case study focuses on residents’ subjective well-being (SWB) and their perceptions of conservation and tourism in two prominent World Heritage Sites (WHSs) in Turkey, i.e. (rural) Cappadocia and (urban) Istanbul’s Historical Peninsula, to evaluate them in tandem with their governance frameworks. A modified SWB survey instrument is administered to residents in both WHSs. The most notable differences in SWB surface in community, environment, and standard of living domains favouring Cappadocia – underlining tourism’s varied influence on SWB domains in rural and urban settings. While the perceived association between conservation and tourism is stronger in Cappadocia, Istanbul’s residents view conservation more positively. These differences point towards conservation’s perceived instrumental role in facilitating tourism development in rural WHSs and the disconnection of urban WHS residents from conservation efforts and tourism management. Our findings suggest SWB may complement tourism impact analysis and build a bridge between residents, policymakers, and administrators. Addressing the disconnection between residents and tourism and conservation-related frameworks and initiatives to increase resident awareness of heritage value are suggested as potential contributors to the long-term viability of WHSs as heritage tourism destinations.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.011
GPT teacher head0.300
Teacher spread0.289 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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