Navigating knowledge and ignorance in the boardroom: A study of audit committee members' oversight styles
Bibliographic record
Abstract
Abstract Using data collected from 21 interviews with audit committee members (ACMs) of Canadian reporting issuers, this study examines the ways in which ACMs understand and enact the additional responsibilities placed on them by regulators in the post–Sarbanes‐Oxley Act era. Adopting a social constructivist approach to knowledge and expertise, the study shows that despite the financial literacy requirements for ACMs, financial expertise is far from being uniformly understood by ACMs. Indeed, ACMs perceive expertise in many different ways, which leads them to engage in a wide variety of practices to fulfill their responsibilities on audit committees (ACs). The analysis of the data makes it possible to identify three oversight styles—observing, inspecting, and storytelling—that illustrate the differences in how ACMs understand their role, prepare for AC meetings, invest time in this preparation, and develop lines of questioning. These findings provide empirical insights into both the substantive and symbolic roles of ACs and illustrate the role of knowledge and ignorance in shaping ACMs' understanding of their oversight role. This study also raises questions about the soundness of having ACs oversee multiple different processes. By highlighting that ACMs do not comprehend and enact their role uniformly, this study reveals the important nuances in ACMs' oversight approaches.
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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 | 0.024 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".