Effectiveness of Internal Control System as Early Detection Tool in Fraud Prevention of Village Fund Management
Bibliographic record
Abstract
Introduction:The Village Fund is a form of trust from the central government to plan and implement programs and activities in its village.The increasing number of village funds disbursed by the government is not in line with good management. Research Objectives:To analyze the effectiveness of the internal control system as a tool for detection early in preventing fraud in the management of village funds.Methods: This research is qualitative.The object of this research is eight districts and 30 sub-districts as research samples in Central Java Province.This study's data analysis techniques were done through data reduction, presentation, and conclusion drawing. Conclusion:From the results of data analysis in the field, it can be concluded that fraud in the management of village funds is generally caused by the non-functioning role of village assistants in managing village funds.Weak guidance and supervision from various parties that should be the duties and responsibilities of the agency, including the Subdistrict head, the Government Guard and Security Team and the Development of the Semarang District Attorney, the Body Consultative Village, which seems to be just a formality and most importantly the weak community participation, which is caused by lack of understanding of the community towards village development budgets and plans and incompetence of human resources (HR) managing village funds and village heads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".