Impacts of Village Funding on Community Empowerment and Poverty in Klungkung, Bali
Bibliographic record
Abstract
This research aims to evaluate the impacts of village fund allocations on economic empowerment and poverty reduction among low-income populations in Klungkung Regency, Bali.Utilizing a mixed-method approach, the study engaged 259 participants across 37 villages.Methods included observations, interviews, and in-depth interviews for data gathering.Analytical techniques encompassed descriptive statistics, mean difference tests, and path analysis.Key findings reveal that: (1) village funds significantly bolster economic empowerment for low-income groups; (2) while village funds alone do not directly reduce poverty levels, economic empowerment contributes to a marked decrease in poverty; (3) economic empowerment serves as a complete mediator in the relationship between village funds and poverty reduction; (4) the program's implementation exhibits strengths, weaknesses, opportunities, and threats which collectively influence its effectiveness; (5) post-program poverty levels show a notable decline compared to pre-program figures.This study underscores the scarcity of research measuring community empowerment and poverty in relation to village funds, highlighting its potential as a policy-making reference.Contributing to regional economic development literature, this study supports the attainment of Sustainable Development Goals (SDGs) through an integrative analysis of village fund efficacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".