STRATEGI PENGEMBANGAN EKOWISATA DI TAMAN NASIONAL KELIMUTU
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 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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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