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Shopping tourism and destination development: Dubai as a case study.

2014· article· en· W2099493595 on OpenAlexvenueno aff
Naeema Alhosani, Esmat Zaidan

Bibliographic record

VenueArab world geographer · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismBusinessAdvertisingMarketingSample (material)Quality (philosophy)Variety (cybernetics)GeographyComputer science

Abstract

fetched live from OpenAlex

Tourism has been and continues to be an important source of income for many countries in the world. This study explores the concept of shopping in Dubai tourism and identifies the perceived important characteristics of Dubai as a shopping destination, as well as tracing the factors or the competitive advantages of Dubai as a shopping destination for Saudis tourists, who represent Dubai's main source of tourism. Shopping patterns among Saudi tourists are identified based on their frequency of shopping. A survey was used to obtain primary data that included a sample of 1 111 Saudis tourists visiting different shopping places in Dubai. The results show that Saudi tourists define Dubai as their top shopping destination because they perceive it as offering significant advantages: a wide range of impressive retail services, high-quality shopping malls, a wide variety of products and shopping venues, substantial shopping promotions and bargains, unique annual shopping festivals, the creativity of combining the s...

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)0.001
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 score0.058
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.025
GPT teacher head0.321
Teacher spread0.297 · 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

Citations11
Published2014
Admission routes1
Has abstractyes

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