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Record W7128508111 · doi:10.64903/1480-6800-26.2.142

Tourist, Local, and Broker Perceptions of Abu Dhabi and Dubai, United Arab Emirates: Tourist Gazes, Ultra-Artifacts, and Hyperreality

2023· article· W7128508111 on OpenAlexvenueno aff
Harshitha Sai Viswanathan, Marc Miller

Bibliographic record

VenueArab world geographer · 2023
Typearticle
Language
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTourismAbu dhabiTourism geographyExperiential learningGlobalizationWork (physics)

Abstract

fetched live from OpenAlex

The globalization of tourism has dramatically influenced the construction of destinations, as well as the demand for novel experiences in such places as the Emirate of Abu Dhabi and Dubai in the United Arab Emirates. As tourism providers work to immerse visitors in innovative tourism sites characterized by wondrous architecture with extreme technological enhancements, it becomes critical to develop a new and comprehensive understanding of experiential travel. This study employs a mixed-methods research design to develop insights on concepts of tourist gazes, ultra-artifacts, and hyperreality by delving into processes and interactions between brokers, locals, and tourists within natural and built environments. Tourism in Abu Dhabi and Dubai entails a dynamic interaction between ultra-artifactual components and travelers in the pursuit of contrast. Tourism gazes facilitate immersive experiences of hyper-real tourism spaces. This research contributes to Urry’s sociological studies on tourist gazes by introducing a new set of ‘hyper-gazes’ that capture individuals’ stimulating encounters that redefine reality. Findings underscore the importance of perceptions in understanding the design of hyperreal productions and tourism development in the future.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0020.004
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.292
Teacher spread0.270 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

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