The Optimization Management of Special Autonomy Funds for Acehnese People Welfare
Bibliographic record
Abstract
This research aimed to examine the factors contributing to the less optimum management of the Aceh special autonomy funds for the development and welfare of the Acehnese people. Specifically, Aceh received the special autonomy funds of IDR 56.67 trillion from 2008 to 2018, yet the vast funds have not been successful in change the face of Aceh in terms of poverty, unemployment, and other social diseases. This study employed a descriptive qualitative research design and data collection involved interviews, observation, and document study. The informants were selected by purposive and snowball sampling. The results of the study showed that three factors contributed to the less optimum management of the Aceh special autonomy funds. First, the regulation of the Aceh special autonomy funds management has not been standardized and frequently changed, and thus it cannot be used as a complete guideline. Second, the management authority of the special autonomy funds was unclear between the provincial and the district/city government, resulting in no good coordination between the parties. Third, the poor management of the Aceh special autonomy funds led to the poorly targeted development and community empowerment. Based on the findings, it can be concluded that these three factors hindered the Aceh special autonomy funds from fulfilling the goals of realizing the development and welfare for the Acehnese people.
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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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".