Audit Committee’s Role in Moderating the Effect of Financial Distress Towards Going Concern Audit Opinion
Bibliographic record
Abstract
Auditors are required to provide services in auditing financial statements presented by management and provide audit opinions on the fairness of the presentation of financial statements. Sometimes, in reality, management and auditors have their interests so that each party can achieve the goals. The conflict of interest appears on the independence of the auditor in issuing audit opinions, especially the audit opinion with the going concern explanatory paragraph. The audit committee, as a supervisory board, will continue to maintain the independence of the auditor in issuing audit opinions with the going concern explanatory paragraph. This study aims to examine the effectiveness of the audit committee in maintaining the independence of external auditors issuing audit opinions, especially the audit opinion with the going concern explanatory paragraph. Based on the purposive sampling method obtained a sample of 168 companies. Testing the hypothesis in this study using logistic regression and moderated regression analysis. The results showed that financial distress had a significant negative effect for the audit opinion with going concern explanatory paragraph. Furthermore, the audit committee was not able to strenghten the relationship of financial distress on the audit opinion with a going-concern explanatory paragraph.
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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 | 0.018 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".