Evidence About Discipline Committees and Professional Misconduct of Auditors <sup>*,‡</sup>
Bibliographic record
Abstract
ABSTRACT Self‐regulating professions establish professional discipline processes to determine whether members' behavior falls short of expectations outlined in their respective codes of conduct and to determine appropriate sanctions when necessary. From an auditing perspective, audit quality is of primary interest to audit researchers, yet few prior studies have examined how the auditing profession itself assesses and sanctions deficient auditors who likely fail to meet audit quality expectations. To provide a new direction in auditing research, this study focuses on qualitative data published in auditor professional disciplinary proceedings and uses content analysis to examine auditor professional misconduct incidents, disciplinary processes, and disciplinary outcomes. Our analysis of audit deficiencies produces novel insights to align future research more closely with the judgment failures occurring in practice. Importantly, our analyses of the audit profession's disciplinary process and outcomes provide insights into (1) the accounting and/or auditing particulars that compromise auditor judgment, resulting in allegations of professional misconduct; (2) the behavior and justifications of the auditor defendants during the proceedings; and (3) the professional judgment exercised by the auditing profession's discipline committees when ascertaining guilt and determining sanctions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".