Audit committee pre‐Enron efforts to increase the effectiveness of corporate governance
Bibliographic record
Abstract
Purpose This study of audit committee effectiveness, performed in the period immediately preceding the Enron collapse, seeks to determine whether audit committees were beginning to accept more responsibility for corporate governance before such behavior became mandatory. Design/methodology/approach The period studied was approximately two years prior to the Sarbanes‐Oxley Act of 2002 and roughly one year after the Blue Ribbon Committee published its recommendations on audit committee effectiveness. The efforts of 296 audit committees to improve their effectiveness as reported by Chief Audit Executives (CAEs) to the Global Audit Information Network (GAIN) database maintained by the Institute of Internal Auditors (IIA) were investigated. Findings It was found that audit committees' responsiveness to each of eight effectiveness steps was surprisingly high. For instance, almost all (w99.6 percent) audit committees meet with CAEs. It is recommended that audit committees focus more on big picture/strategic concerns in their discussions with CAEs. Research limitations/implications The study's chief limitation is that only companies with internal audit functions were studied and thus the results cannot be generalized to companies without internal audit functions. Originality/value This study was the first to utilize the GAIN database and provides specifics about 15 different topics that CAEs might bring to audit committees for discussion. Topics of communication more often focused on specifics such as “significant audit findings” (95.9 percent) and less often dealt with big picture/strategic concerns such as “overall corporate control environment” (68.9 percent).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.107 | 0.192 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".