Is the <scp>PCAOB</scp> enforcement approach aligned with its mandate? Perspectives of sanctioned auditors and former <scp>PCAOB</scp> enforcement staff
Bibliographic record
Abstract
Abstract The Sarbanes‐Oxley Act of 2002 mandates the PCAOB to enforce compliance with its audit standards fairly. However, the enforcement process is not sufficiently transparent for public evaluation of its fairness, prompting a call by a former Board member for transparency of the process and for improvement suggestions from the public. Further, academic evidence on the PCAOB enforcement is limited. We address this call and the gap in the literature by interviewing 33 difficult‐to‐access participants about the enforcement process: 20 sanctioned auditors and 13 former PCAOB enforcement staff members. Using procedural justice theory as a lens in evaluating our data, we conclude the enforcement process lacks fairness in key components. Both auditors and former enforcement staff express concerns that staff use overly damning one‐sided language in public orders, do not assess investor harm, and face incentives to sanction auditors, particularly small firms that cannot afford costly defense. We contribute to the literature on PCAOB enforcement by offering new insights into the enforcement process from firsthand perspectives of sanctioned auditors and former enforcement staff, deepening understanding of how enforcement practices align with the PCAOB's mandate for fair procedures. We also discuss process improvement suggestions from our participants and important future research opportunities.
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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 | 0.050 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".