Developing a Decision Support System for Sustainable Management of Community-Based Ecotourism: A Case Study of CMC Tiga Warna
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".