Audit Committee Accounting Expertise and Audit Quality – the Case of Going-Concern Opinions
Bibliographic record
Abstract
This study examines whether audit committee accounting expertise and other audit committee characteristics promote or deter the likelihood of receiving going-concern reports from the auditors and whether such characteristics shield auditors from dismissals after the issuance of a going-concern report. The study finds no significant association between the likelihood of a going-concern report and audit committee accounting expertise or other audit committee characteristics. No significant association is also found for auditor dismissals following going-concern reports and audit committee accounting expertise. These results contrast with prior literature that examined data preceding the passage of the Sarbanes-Oxley Act of 2002 (hereafter SOX) or the period immediately thereafter. Additional analysis shows that audit committee accounting expertise is found to improve the information in going-concern audit opinions by reducing Type I errors, however. Overall, these findings shed light on the evolving role of audit committees in overseeing the auditors and have implications for regulators interested in improving audit quality and investors interested in improving the effectiveness of audit committees.
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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 | 0.023 | 0.217 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".