MétaCan
Menu
Back to cohort
Record W4407777576 · doi:10.3390/tourhosp6010034

Quality Tourism in Thailand: Towards Sustainable Tourism or Further Wealth Concentration?

2025· article· en· W4407777576 on OpenAlexafffund
Alexandre Veilleux, Bruno Sarrasin

Bibliographic record

VenueTourism and Hospitality · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversité du Québec à MontréalUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaUniversité du Québec à Montréal
KeywordsTourismBusinessSustainable tourismQuality (philosophy)Natural resource economicsAgricultural economicsGeographyEconomics

Abstract

fetched live from OpenAlex

In its long-term vision for tourism development (2017–2036), Thailand has chosen to focus on the development of quality tourism, which is supposed to enable sustainable tourism development and a more inclusive sharing of tourism-generated revenues. However, the use of the term “quality tourism” remains conceptually unclear, and the means by which quality tourism will enable a more inclusive sharing of wealth remain ambiguous. Taking the tourist island of Phuket as a case study, we question how quality tourism has materialized on the island and how it has affected the configuration of power between large international hotel chains and local hotel operators regarding tourism development. Guided by a critical political economy framework and based on a qualitative methodology involving triangulation of data collection among official documents, semi-structured interviews, and participant observation, we argue that quality tourism in Phuket, although justified as a form of sustainable tourism, is more akin to luxury tourism. This has led to greater concentration of wealth among large hotel chains and real estate groups who have taken advantage of quality tourism-related policies to boost their portfolios at the expense of local stakeholders.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.443
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.367
Teacher spread0.345 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueTourism and HospitalitySame topicDiverse Aspects of Tourism ResearchFrench-language works237,207