The Association Between Accruals Quality and the Characteristics of Accounting Experts and Mix of Expertise on Audit Committees*
Bibliographic record
Abstract
An important dimension of audit committee (AC) effectiveness that has gained the attention of regulators and academics is the financial expertise of AC members (General Accounting Office 1991; Public Oversight Board 1993; Kalbers and Fogarty 1993; DeZoort 1997, 1998; Blue Ribbon Committee on Improving the Effectiveness of Corporate Audit Committees 1999; DeZoort, Hermanson, Archambeault, and Reed 2002; Sarbanes-Oxley Act of 2002 [SOX] 2002; Cohen, Krishnamoorthy, and Wright 2004). Section 407 of SOX requires the Securities and Exchange Commission (SEC) to adopt rules mandating that the AC of public firms include at least one member who is a financial expert or disclose reasons for not adopting this requirement. While SOX proposes a narrow definition of financial expertise, to include individuals with experience in accounting or auditing, the SEC controversially adopted a broader definition of financial expertise that includes accounting and certain types of nonaccounting (finance and supervisory) financial expertise ....
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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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".