Evaluation on Implementation of Whistleblowing System in State Development Audit Agency
Bibliographic record
Abstract
The purpose of this study is to measure the effectiveness of institutional whistleblowing systems. The method used is quantitative descriptive. In this study, the sample used was the user of the whistleblowing system of the Financial and Development Supervisory Agency (BPKP), especially internal users. The data used in this study is the results of questionnaire data that has been distributed and filled out by BPKP employees. The questionnaire was designed based on Theory of Planned Behavior. The results of this study are in the form of a questionnaire design and the level of effectiveness of the whistleblowing system at BPKP. By using the Theory of Planned Behavior, there are three important aspects underlying the effectiveness of the whistleblowing system, namely the training and communication aspects, aspects of transformational leadership, and aspects of top management support. The level of effectiveness of the whistleblowing system at BPKP, especially internal whistleblowing, is 62.8%. The effectiveness level of 62.8% reflects that the whistleblowing system at BPKP is quite effective.
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 | 0.024 | 0.048 |
| 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 | 0.000 | 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".