Designing pathways towards sustainable tourism in Soka Tourism Bali: A MULTIPOL policy analysis
Bibliographic record
Abstract
Sustainable tourism is known as an effort to prevent the negative impacts of tourism development by considering economic, social and environmental aspects. Sustainable tourism development is very important to be used as a reference in managing tourism destinations. The large tourism potential in the Bali Province has been inventoried by the Provincial Government of Bali by establishing a tourism area. However, in its development, several designated tourism areas cannot develop optimally and tend to experience a decrease in the quality and quantity of tourist visits. This paper presents transformation pathways toward sustainable tourism development in tourism areas. The general objective is to determine strategies that can be pursued in the development of tourism areas. The specific objective is to develop the best policies and scenarios in the development of tourism areas. This research was conducted in the Soka Tourism Area in Bali Province, Indonesia, which is the only strategic tourism area in Tabanan Regency, which has great potential but has not been able to develop optimally. The data obtained from the focus group discussion, as a data collection method will be analyzed using the MULTIPOL method. The results of the research show that the development of sustainable tourism areas can be carried out by preparing a clear and measurable framework. Promotion, preparation of cross-sector programs and tourism management training activities are needed as priority programs. On the policy, it is necessary to carry out effective planning as an optimal policy involving all stakeholders. In addition, the “Progressive Transformation” and “Integrated Transformation” scenarios can be considered to achieve sustainable tourism development. This study can be an important input for stakeholders in determining policies for the development of tourism areas in the research locations and can be applied in other areas that have similar characteristics.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".