After Seven Decades of Regulation, Why is the Audit Profession in Such a Mess?
Bibliographic record
Abstract
On December 2, 2001, Enron, once one of the world's largest and most re spected companies, filed for Chapter 11 bankruptcy protection after the discovery of a massive fraud perpetrated by senior management. The subse quent collapse of Enron's auditor, Arthur Andersen LLP (AA), was even more shocking. Mr. Arthur Andersen had built an accounting practice on a solid foundation of personal integrity and technical excellence. He had a reputation of being absolutely honest and is reputed to have lost several prominent clients because he would not change his audit report to suit management. Famous sayings attributed to him are There is not enough money in Chicago to cause me to change my audit report, and Think and Talk Straight (Squires et al., 2003, pp. 31-32). Fifty-five years after his death in 1947, the firm of Arthur Andersen, LLP (AA) was engulfed in the Enron fiasco.
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".