Revitalization of Village-Owned Enterprises to Strengthen the Community Economy in Indonesia: Between Policy and Prosperity
Bibliographic record
Abstract
Indonesian president-elect, Prabowo Subianto, has emphasized his vision through Astacita, continuing President Joko Widodo’s Nawacita concept. The primary focus is on village development and community economic empowerment to reduce inequality and poverty. This study employs a qualitative descriptive approach, analyzing legal data, regulations, and philosophical, political, and economic perspectives related to Village-Owned Enterprises (BUMDes). The main objective is to explore the urgency of revitalizing BUMDes to encourage village development, improve the local economy, and align with policies aimed at fostering community-based welfare. The Village Law strengthens the role of villages in development, with BUMDes acting as a key driver of the village economy. However, BUMDes faces challenges in management and competitiveness. To address this, revitalization and collaboration are necessary to increase productivity, leverage local potential, and support community welfare. The government must enhance policies, improve management capacity, and protect BUMDes from harmful competition. Digitalization and synergy between villages also present solutions to bolster the village economy in the Industry 4.0 era. BUMDes plays a crucial role in boosting the village economy based on local potential, but participatory, transparent, and professional management is essential for its independent development. Strengthening subsidiarity and village authority accelerates sustainable development, in line with global trends in enhancing local governance and village economic autonomy. The revitalization of BUMDes is a key strategy for improving the village economy through the optimization of village funds and digitalization. Addressing management challenges requires enhanced capacity for village officials, strict supervision, and inclusive policies to foster sustainable economic growth and community welfare
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".