Whistleblowing System and Fraud Early Warning System on Village Fund Fraud: The Indonesian Experience
Bibliographic record
Abstract
This research aims to investigate the effectiveness of village fund fraud prevention models by analyzing the implementation of the Fraud Early Warning System (FEWS) and whistleblowing system to good village governance towards clean government. This study used a descriptive qualitative research method by conducting interviews to explore more information about the problems of preventing village fund fraud. The paradigm used is the interpretive and methodology paradigm used to express meaning is phenomenology to describe and explain how behavior in the implementation of FEWS and the whistleblowing system against village fund fraud. Determination of informants was carried out with a sequential technique, namely all village officials and communities involved in managing the process of allocating village funds in Sumowono Subdistrict, Central Java Province, Indonesia as research informants. The population of this study was 105 village officials and community members from 16 villages in Sumowono District. This study shows that in managing village fund fraud, complaints about village fund fraud were mainly driven by courage from the local community in their respective villages. The strategy to reduce fraud is to provide opportunities for the community to implement FEWS and the whistleblowing system as a preventive strategy to prevent village fund fraud. FEWS and wshistleblowing system activities in village funds also face various challenges. The implementation of the FEWS and the effective whistleblowing system, the fraudsters will think again whether to continue fraud or cancel the behavior.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".