Getting Inside the Black Box: A Field Study of Practices in “Effective” Audit Committees
Bibliographic record
Abstract
Although audit committees typically are considered a crucial corporate governance mechanism, knowledge is scant about the practices carried out in audit committee meetings. This paper provides insights into practices that audit committee members carry out in meetings, including the part of the meetings where members meet privately with auditors. The investigation was conducted via a field study in three Canadian public corporations—whose respective audit committees complied to a large extent with regulatory guidelines of the Toronto Stock Exchange and the voluntary recommendations of the Blue Ribbon Committee on audit committee effectiveness. Further, the three audit committees that we investigated are generally perceived as effective by the individuals who attend meetings. Our results highlight key matters that audit committee members emphasize during meetings, such as: accuracy of financial statements; appropriateness of the wording used in financial reports; effectiveness of internal controls; and the quality of the work performed by auditors. We also elicit the evaluation criteria that members use to assess written and verbal information submitted by managers and auditors. In addition, we found that a key aspect of the work carried out by audit committee members consists of asking challenging questions and assessing responses provided by managers and auditors.
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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 | 0.040 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".