Bibliographic record
Abstract
This review aims to sniff out potential fraud in controlling village funds and to find out effective mechanisms for preventing village fund fraud in Indonesia. However, apart from contributing institutions that were small, previous researchers have ignored the problem of fraud shortly threatening sustainability of institutions such as a small rural village in Indonesia. So, this study is intended to find out how a small village level institution can prevent fraud. This analysis uses a self-administered questionnaire and distributes 250 questionnaires to village heads, secretary of village heads, and financial treasurers in village institutions with 179 questionnaires for which data can be processed. To test the theoretical model, multiple regression is used. Outputs from multiple regression reveals that a habit of honesty and integrity have a positive effect and significant, process and control the internal and supervisory functions are good and behavioral religious has a positive effect but are less significant in the fraud preventive mechanism if implemented partially. This finding also provides a strong picture that if the four dimensions of fraud prevention mechanism must be implemented simultaneously to have high effectiveness and vice versa. On the whole, the research paper is advocating some tactics to prevent fraud which is effective to reduce the threat of fraud in the institution at the smallest village level in Indonesia and the countries of the developing others. The lack of studies empirically the impact of habits of honesty, internal control, and monitoring Duty proper religious behavior and attitudes in an effective study of fraud prevention in non-Western environments has answered the need for this research.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".