Audit Review: Managers' Interpersonal Expectations and Conduct of the Review*
Bibliographic record
Abstract
Abstract This paper presents an interpersonal model of audit file review centered on the audit manager. A manager's conduct of the review is affected by four components: the manager's expectations about the client, expectations about the preparer, expectations about the partner, and the manager's own approach and circumstances. The paper then presents a comprehensive field‐based analysis of how a working paper review is conducted. It supplements the mostly experimental research on working paper review by reporting the results of a retrospective field questionnaire that asked audit managers to report on their behavior and their relationships with preparers and partners on actual audit engagements. The extent of review was sensitive to specific features of the client and the file (including risk factors), to features of the preparer, and particularly to the style of the reviewer, which was quite stable across cases. Although the evidence of managers' awareness of preparers' “stylizing” the file to suit the manager was weak, the evidence of managers' stylizing for the partners was pervasive, affecting both work done and documentation. Managers believed that good reviews emphasized key issues and risks rather than detail. Other new descriptive evidence on the nature of the review process is provided, including the purpose of the review process, how frequently surprises are found in the review process, and the qualities of good reviewers compared with poor reviewers. The implications of our model and our results for future research are outlined.
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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 | 0.045 | 0.243 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".