Tanjung Datu National Park, Sarawak: Communities’ Perceptions towards Its Rural Competitive Advantage
Bibliographic record
Abstract
Rural tourism is a rapidly growing tourism segment and has been given increasing importance, in view of its potential to contribute economic growth to the rural areas. However, any rural tourism destination development should be implemented in a way that maximizes destination competitive advantage. This study examine the relationship between stakeholder involvement, community knowledge about tourism, and the economic, socio-cultural and environmental impacts on tourism with rural competitive advantage, as perceived by the local community. This study obtained, as voluntary respondents, 87 residents of a rural tourism destination in Sarawak, Malaysia. 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. The findings suggested that stakeholder involvement in tourism, community knowledge about tourism and the environmental impact of tourism have had a significant positive impact on rural competitive advantage, whereas economic and socio-cultural impacts of tourism, as well as community support for tourism, had little impact on the same. Lastly, such study would benefit the tourism implementers and decision-makers in tourism industry, in the sense that tourism activity can be proactively detected and curbed once critical mass is reached.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".