The Composition of Independent Board of Commissioner and Number of Board of Commissioner Meeting Towards Fraudulence of Financial Report (Empirical Study at Public Company Listed at Indonesia Stock Exchange in 2011-2017)
Bibliographic record
Abstract
This study aims to obtain empirical evidence about the effect of corporate governance mechanisms on fraudulent financial reporting. The variables of corporate governance used are independent board composition, frequency of board commissioner meetings, and external auditor quality as moderating variables between the influences of independent board composition, number of board of commissioners meetings against fraudulent financial reporting. The population of this study was public companies listed on the Indonesia Stock Exchange in 2011 - 2017. The total samples of this study were 76 companies, consist of 38 companies reported committing fraudulently financial statements and 38 companies that did not cheat financial statements. Data analysis was carried out by descriptive analysis, crosstab and hypothesis testing using the logistic regression method. The results of this study indicate the composition of the independent board of commissioners and the frequency of board of commissioners meetings has a significant and negative effect on the fraudulent financial report. Also, the quality of external auditors can strengthen the influence of the composition of the independent board of commissioners and the number of board of commissioners meetings on the fraudulent financial reporting.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".