The Role of Local Government on Rural Tourism Development: Case Study of Desa Wisata Pujonkidul, Indonesia
Bibliographic record
Abstract
Tourism has contributed significantly to economic growth, and the government is the leading actor in the tourism development process. This article aims to discuss the role of village government in rural tourism development. The research was conducted qualitatively with a case study in Pujonkidul, a tourist village that is growing and developing into a rural tourism destination rapidly in Indonesia. The research data were collected through a series of in-depth interviews with village governments and main actors in the rural tourism development process. Observation and study of document also carried on during the process of collecting data at the village. The result of study show that the local government is able to carry out all government functions in tourism development (coordination, planning, regulation and legislation, entrepreneurship, stimulus and promotion, social tourism role and boarder role of interest protection). This study also found a new function of government in tourism development which is the main finding of this study, namely institutional development. Therefore, the authors argue that the village government can conduct rural tourism development locally and effectively with its functions and authorities. This finding of study can be adopted and developed in the other villages in the process of rural tourism development. The limitation of this study has ignored the discussion of villagers' participation in the rural tourism development process that is the essential form of rural development issues. This limitation is an important topic for future research.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".