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Record W2180521035 · doi:10.5539/jsd.v8n9p254

Studying the Motivations of Choosing Foreign Tourism Destinations: Case Study of Thailand

2015· article· en· W2180521035 on OpenAlexvenueno aff
Maryam Mosahebipoor Fard, Hamid Saberi

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsDestinationsTourismOrder (exchange)Tourist attractionTourist destinationsMarketingPopulationCurrencyAdvertisingTasteBusinessGeographyDescriptive researchPsychologySociologyEconomicsDemographySocial science

Abstract

fetched live from OpenAlex

Nowadays tourists choose their destinations according to their taste, conditions and provided facilities. Aimed at psychologically understanding the reasons of travelling and the attraction of trip destinations, the scientists have presented different patterns for the travelling and the choice of trip destination. This study has been done in order to realize the motivations of choosing Thailand as a trip destination by tourists departed from Isfahan. The study is methodologically descriptive-analytic and is done in 2014. In total, 384 people were chosen through Cochran formula among the whole population –including all the tourists departed from Esfahan to Thailand- in order to achieve the goals of the research. The results of the research show that the existence of cheap attractive centers and proper currency of Iran and Thailand and also on the other hand beach attractions in Thailand affect the choice of this country as a tourist destination but the men’s and women’s motivation of choosing this country as a tourist destination differs significantly.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.348
Teacher spread0.264 · 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 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
Published2015
Admission routes1
Has abstractyes

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