Error Management in Audit Firms: Error Climate, Type, and Originator
Bibliographic record
Abstract
ABSTRACT This paper examines how the treatment of audit staff who discover errors in audit files by superiors affects their willingness to report these errors. The way staff are treated by superiors is labelled as the audit office error management climate. In a “blame-oriented” climate errors are not tolerated and those committing errors are punished. In contrast, an “open” climate characterizes error commitment as a normal, albeit unfortunate aspect of organizational life that offers opportunities for learning without sanctions on the originator. We examine error management climate in the context of audit-specific factors that might affect the decision to report errors: audit error type (conceptual or mechanical) and who committed the error (the individual who discovered it or a peer). An open climate results in an increase in the reporting of mechanical (but not conceptual) errors and all peer errors versus a blame climate. Post hoc findings suggest that one obstacle to reporting conceptual errors stems from an auditor's own impression management concerns. We discuss how auditing standards and regulatory inspections may impact audit firm error management climates. Data Availability: Experimental data are available from the second author subject to data confidentiality restrictions issued by the participating firms.
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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 | 0.017 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".