The Bali Ecotourism Destination Management to Create Local Small Business
Bibliographic record
Abstract
In the case of Indonesia, most of the tourist attractions offered and advertised are national parks or protected forests.They are under protection to be preserved, on the other hand, they are advertised to attract many tourists.In many cases, there is a gap between idealism and reality.It is believed that good ecotourism management can mediate between these two interests.This study aims to determine the Ecotourism Destination Management to Create Local Small Business at related to the five ecotourism destinations, namely West Bali National Park, Lake Buyan Area, Batur Geopark Museum, Bali Mangrove Denpasar, and Lembongan Mangrove Klungkung.This study consists of a survey, direct observation, interviews, and a literature review with documentation analysis.Data were collected through surveys and observations at ecotourism destinations in Bali.Motivation to participate in ecotourism management can be increased by providing management opportunities that can increase community income through the establishment of small businesses related to ecotourism potential.In this context, the government can issue limited management permits to communities with clear rules so that the forest managed as an ecotourism program remains sustainable.The communities' motivation for ecotourism will increase if they have the opportunity to participate in ecotourism management, and for this, they need to improve their ecotourism management skills.If they are motivated, have the opportunity to participate, and can participate, then they will be able to create small business opportunities related to ecotourism programs.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".