Model of effective and efficient village fund management policy in Indonesia
Bibliographic record
Abstract
The Village Fund is a strategic policy to promote equitable rural development and empower local communities in Indonesia. However, despite notable effectiveness—reflected in high fund absorption, improved village status, reduced unemployment, and increased income—its implementation faces significant efficiency challenges due to overlapping regulations, fragmented authority, and poor institutional coordination. This study aims to analyze the factors affecting the effectiveness and efficiency of Village Fund management and to formulate a comprehensive policy model. Using a qualitative descriptive approach, data were collected through literature review, interviews, focus group discussions, and field observations, followed by coding analysis. The findings reveal that macro-level issues such as regulatory dualism and institutional conflicts cascade into micro-level inefficiencies, including the neglect of community empowerment and underutilized village institutions. To address these challenges, the study proposes a conceptual model that integrates the four formal objects of government science—authority, institutional relations, public services, and welfare—with the "one-door" policy approach. This model advocates for centralizing Village Fund authority under one coordinating ministry supported by others, thereby streamlining structure, enhancing public service efficiency, and improving community welfare. The study also emphasizes transparency, responsiveness, technological integration, and inclusive participation as critical components. The implications suggest that while the model offers structural clarity and governance innovation, further empirical research using positivist methods is required to validate and operationalize its impact in practice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".