Revitalizing Ecotourism for a Sustainable Tasik Chini Biosphere Reserve
Bibliographic record
Abstract
Ecotourism is often perceived as an excellent tool for promoting sustainable development in most of the protected and special areas, including the Biosphere Reserve (BR). In fact, ecotourism can help to revive a declining tourist destination as it preserves nature and support rehabilitation and most importantly, it fits well with the Biosphere Reserve functions of conservation, development and logistics. This article aims to analyse the life cycle of Tasik Chini as an ecotourism destination, by focusing on the following aspects, namely travel engagement, ecotourism approaches and revitalization initiatives. This study utilized a mixed method approach by combining the primary and secondary data in tracing the evolution, development and the existing stage of ecotourism. The study reveals that the four major components which drive ecotourism program/initiatives toward achieving sustainable development are the state of ecotourism in the BR, the targeted segments of the community, the extent of community involvement as well as the supportive infrastructure for ecotourism. As ecotourism in the BR significantly promotes learning and research, smart partnership or collaboration between the stakeholders will help generate true eco-tourists. It is therefore imperative that ecotourism initiatives in the context of the BR is revitalised as it will enable a reflective analysis of the destination’s life cycle. The intervention of credible initiatives can fulfil the real and future roles of ecotourism in sustainable development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| 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".