THE QUALITY OF HUMAN RESOURCES OF VILLAGE GOVERNMENT OFFICIALS IN MANAGING VILLAGE FUNDS IN CENTRAL MALUKU REGENCY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it