Financing Rural Futures: Governance and Contextual Challenges of Village Fund Management in Underdeveloped Regions
Bibliographic record
Abstract
Effective management of village funds is central to financing sustainable and equitable rural futures, particularly in underdeveloped and resource-diverse regions such as Papua, Indonesia. This study explores the governance factors that shape the sustainability of village fund management (VFM) by examining institutional, financial, and socio-cultural dimensions across 212 villages. Primary data from village heads and secondary data on village-owned enterprises (BUMDes) and 2024 village fund allocations were analyzed using exploratory factor analysis (EFA), partial least squares structural equation modeling (PLS-SEM), and multi-group analysis (MGA). Seven key governance constructs emerged, with ethical governance, implementation capacity, mandatory disclosure and reporting, community participation, and financial management capacity demonstrating significant positive effects on sustainable VFM outcomes. In contrast, perceived social and economic impacts were negatively associated with performance, and planning quality exerted an influence only under specific contextual conditions. These relationships proved highly context-dependent, varying by geography, natural resource availability, transport accessibility, and demographic composition. The findings underscore the need for adaptive and context-sensitive governance strategies to strengthen institutional resilience, enhance fiscal equity, and maximize the developmental impact of village funds in underdeveloped rural regions.
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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 | 0.001 |
| 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) | 0.001 | 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".