THE QUALITY OF HUMAN RESOURCES OF VILLAGE GOVERNMENT OFFICIALS IN MANAGING VILLAGE FUNDS IN CENTRAL MALUKU REGENCY
Bibliographic record
Abstract
The study focuses on the quality of human resources used by village government officials in managing village funds in Central Maluku Regency. This research uses a quantitative method with a sample size of 60 people. There are two types of data used: primary data and secondary data. Data collection was carried out through questionnaires, observations, and documentation. Sociometric analysis tools were used to analyze the data. The analysis results show that the quality of human resources of village government officials in managing village funds in Central Maluku Regency has not yet met expectations, thus requiring improvement according to priorities, namely: If training and mentoring are conducted properly, village funds will be better managed. If the formation of village fund management groups is carried out well, effective village fund management will take place. If supervision and control are optimally implemented, adequate village fund management will be achieved. If cooperation between the government and the community is well established, the desired village fund management will be created. If the social and cultural aspects of the community are well considered, they will support village fund management. Village fund management can run well if village government officials carry it out transparently, accountably, and participatively, thus supporting the achievement of quality management as a tangible manifestation of the quality of human resources of village government officials in managing village funds. The results and findings of this research have implications for improving the quality of the human resources of village government officials in managing village funds in Central Maluku Regency.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".