Educational Tourism in Malaysia: Implications for Community Development Practice
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".