Evidence from the United States on the Effect of Auditor Involvement in Assessing Internal Control over Financial Reporting
Bibliographic record
Abstract
Securities regulators around the world are considering the costs and benefits of alternative policies for providing information to financial markets on corporate internal control. These policy options differ on the level of auditor involvement, among other dimensions. We examine the association of relative auditor involvement and auditor characteristics with Section 302 internal control disclosures made by US ‘non‐accelerated filers’ from 2003 to 2005. We find more material weaknesses disclosed in the fourth quarter, when there is relatively more auditor involvement, relative to the first three quarters. Clients of larger audit firms have higher disclosure rates (although they are probably less risky due to more stringent client acceptance standards), but this difference is due to fourth quarter disclosures. Audit firms with Section 404 experience also have greater material weakness disclosure, implying process improvement associated with knowledge sharing across engagements. Collectively, our results shed light on ways to increase the effectiveness of internal control regulation.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".