Harmonizing Rural Tourism and Rural Communities in Malaysia
Bibliographic record
Abstract
Malaysia rural tourism is composed of a large number of rural communities, each with distinct and varied assets. Within Malaysia, it is noticeable that tourism demand drivers play an important role in generating trips to rural tourism areas. Nonetheless, there are a number of strengths, weaknesses, opportunities and threats in rural tourism. Clearly, rural tourism marketing efforts need to leverage on the existing strengths and maximize the available opportunities. Hence, the purpose of this research is to investigate the impact of tourism on social, economics, environment and cultural from local communities perspectives in rural setting. 184 respondents comprising of local communities from 34 rural tourism sites in Malaysia took part voluntarily in this study. Twelve hypotheses comprising the dimensions of social, economics, environment and cultural on three components namely, positioning, communities’ value and destination environment were developed.To assess the developed model, SmartPLS 2.0 (M3) was applied based on path modelling and then bootstrapping with 200 re-samples was applied to generate the standard error of the estimate and t-values. Interestingly, the findings suggested that local communities were most concerned on the cultural and social impacts of tourism on their values, repositioning of the destination and environment. The present study provides lessons on the importance of continuing the efforts to understand the impact of rural tourism development from the local communities’ perspectives and to take into considerations views from the local communities in developing rural tourism destination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".