Developing a Decision Support System for Sustainable Management of Community-Based Ecotourism: A Case Study of CMC Tiga Warna
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ecotourism, aimed at appreciating and preserving biodiversity and natural ecosystems while providing economic and social benefits to local communities, faces complexity in management, requiring careful consideration to balance economic, social, and environmental aspects.Decision-making in ecotourism management involves various stakeholders, including government, NGOs, industry players, and local communities.CMC Tiga Warna in Indonesia is a highly potential ecotourism destination but poses challenges in environmental sustainability while meeting the economic and social needs of the local community.Thus, developing a decision support system (DSS) for sustainable community-based ecotourism management becomes crucial.This study aims to develop and implement a DSS based on priority actions, considering biodiversity, local community welfare, environmental and financial sustainability.Utilizing a community-based approach, the study engages local stakeholders and analyzes priority management actions across eight dimensions.Multi-criteria techniques like PROMETHEE will determine the best management actions to address challenges and opportunities for sustainable ecotourism management.The research contributes to sustainable management strategies for ecotourism in CMC Tiga Warna and provides a foundation for similar DSS development in other ecotourism contexts.It underscores the importance of holistic and sustainable ecotourism management for achieving economic development while conserving the environment, serving as a model for creating sustainable ecotourism environments worldwide.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it