STRATEGI PENGEMBANGAN EKOWISATA DI TAMAN NASIONAL KELIMUTU
Bibliographic record
Abstract
Ecotourism development strategy in Kelimutu National Park (KNP) is very necessary because KNP has enormous potential for ecotourism development. The potential is either in the form of flora, fauna, geology, environmental beauty, and cultural potential of the surrounding community. The aims of this study are to know the right strategy in ecotourism development and determine the priority scale of ecotourism pathways development in KNP. This study uses a case study approach. Data were collected through in-depth interviews of KNP management, stakeholders in the management of ecotourism of KNP, communities around KNP, and observation. The data were analyzed using stakeholders analysis to determine the stakholders that involved on ecotourism management in KNP, SWOT (Strength, Weakness, Opportunities, Threats) analysis to determine the right strategy in ecotourism management, and AHP (Analysis Hierarcy Process) to determine the priority scale of ecotourism development from several ecotourism pathway in KNP. The results show that the most appropriate strategy in the development of ecotourism in KNP is offensive strategy (taking advantage of opportunities and strengths owned), and ecotourism pathway that get the first priority to be developed is the Moni Pathway, the second is Wologai Pathway, the third is Sokoria Pathway, and the fourth is Niowula Pathway.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".