Whistleblowing System Intention Internal Auditors in the Provincial Government of Dki Jakarta
Bibliographic record
Abstract
The study delves into the phenomenon of whistleblowing within the Jakarta Special Capital Region Government. Whistleblowers expose instances of corruption, financial anomalies and collusion by providing evidence through photos and videos, on social media platforms. This condition is exacerbated by the tendency on the part of the Government to not be serious about disclosing or on the part of the reporter there is a tendency to be afraid if there is a backlash against him. The aim of this research is to test the exogenous latent variables Perceived Organizational Support (POS), Capability (CAP), Attitude Toward Whistleblowing (ATW), Rationalization (RAS), Opportunity (OPP), Pressure (PRES), and the endogenous latent variable Whistleblowing System Intention (WBSI), and also the mediating variable is Perceived Behavioral Control (PBC). The method used was a sample of 450 employees working in the DKI Jakarta Provincial Government offices, and data processing was carried out using the Structural Equation Model. The test results for the Perceived Organizational Support, Capability, Attitude Toward Whistleblowing variables have a direct and significant positive influence on the Perceived Behavioral Control variable. The Whistleblowing System Intention variable is influenced directly and mediated by the Perceived Behavioral Control variable which is significantly positive, Rational is positively significant and Opportunity is positively significant. Meanwhile, the Pressure variable has a significant negative influence on the Perceived Behavioral Control variable and the Whistleblowing System Intention variable.
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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".