The Relationship between Board Characteristics and Voluntary Improvements in Audit Committee Composition and Experience*
Bibliographic record
Abstract
Abstract This study empirically examines the relation between certain board of director characteristics and the extent that audit committee composition voluntarily exceeds minimum mandated levels and includes outside directors with financial reporting and audit committee knowledge and experience. This study focuses on board characteristics because the board directly controls audit committee membership. Such staffing decisions can directly affect the ability of the audit committee to monitor management's financial reporting process on behalf of the board. Results suggest that Canadian firms that voluntarily include more outside directors on the audit committee than the mandated minimum have larger boards with more outsiders serving on those boards and are more likely to segregate the board chairperson position from the CEO/president positions. Additionally, firms that voluntarily create audit committees composed of outsider members with a breadth of relevant financial reporting and audit committee knowledge and experience have boards that are larger, have more outside members, and are less likely to be chaired by the CEO/president. Implications of these findings for auditors, institutional investors, regulators, and other interested parties are discussed.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".