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Record W2022650561 · doi:10.5539/ass.v9n11p17

Educational Tourism in Malaysia: Implications for Community Development Practice

2013· article· en· W2022650561 on OpenAlexvenueno aff
Asnarulkhadi Abu Samah, Maryam Ahmadian

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
FundersUniversiti Putra Malaysia
KeywordsTourismProsperityChristian ministryKuala lumpurEconomic growthGeographySocioeconomicsQuestionnairePolitical scienceMarketingBusinessSociologySocial scienceEconomics

Abstract

fetched live from OpenAlex

Background: This paper aims to discuss the impacts of educational tourism on the residents in the Klang Valley, Malaysia. Educational tourism is a means to empower educational communities, host communities, local entrepreneurs, and to enhance economic prosperity. Method: A cross-sectional survey was carried out in five districts in Selangor and Kuala Lumpur (Federal Territory) which are located in the Klang Valley, and Nilai, Negeri Sembilan. Data was collected from 700 respondents using structured questionnaire from the Klang Valley and Nilai. Results: Pearson correlation analysis revealed that, there is significant positive moderate relationship between attitude, environmental impact, economic impact, and socio-cultural impact and practice. Multiple linear regressions showed that socio-cultural and economic impact of educational tourism, and local community attitude has significantly contributed to residents’ practice to educational tourists in the Klang Valley. Conclusion: The findings propose new insights into the socio-cultural and economic impacts of educational tourism and community development practice. Educational tourism can sustain the development of career paths for workers and local communities. The results of this study present practical information on sustainable educational programs for both the Ministry of Higher Education and the Ministry of Tourism.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.052
GPT teacher head0.407
Teacher spread0.355 · 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.

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
Published2013
Admission routes1
Has abstractyes

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