Village Owned Enterprises Governance (BUMDes) Based on the Tourism Village Development
Bibliographic record
Abstract
Tourism villages are suspected of having broad and multiplier effects on the lives of rural communities, including reducing poverty, preventing urbanisation, namely the movement of people from villages to cities, reducing unemployment, increasing the income of rural communities, regions and even countries, preserving the environment, culture local and love the motherland more.To such an extent the positive impact of tourism villages on rural communities has resulted in the attention of the government of the Republic of Indonesia currently being increasingly focused and intense on the development of tourist villages, both in the form of programs and activities as well as allocation of village funds, especially for the development of tourist villages.This study analyses the village-owned enterprises (BUMDes) based on the tourism village development in Samosir North Sumatera.The technique of data analysis used multiple regression analysis.The research result showed that there was a significant and positive contribution between the village-owned enterprises (BUMDes) on the tourism village development in Samosir North Sumatera.There was a contribution between the openness village-owned enterprises model, the transparency village-owned enterprises, accountability village-owned enterprises, fairness village-owned enterprises, independent village-owned enterprises (BUMDes) on the tourism village development in Samosir North Sumatera.The new findings of this research based on the interviews conducted were in addition to governance of openness, transparency, accountability, fairness.The new findings of this research were participation, entrepreneurial, and social capital governance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".