Audit Review: The Impact of Discussion Timing and Familiarity
Bibliographic record
Abstract
I investigate how the trend in audit practice of including face-to-face discussions between the preparer and the reviewer affects audit team performance. Specifically, I focus on the timing of reviewer/preparer discussion and explore whether performance of the audit team in a task involving a review by interview process is affected by the timing of the discussion. The discussion timing compares senior/manager teams when review discussions are held either concurrently with or following the manager's review of the senior's work. Additionally, I explore how reviewers' familiarity with preparers may also affect the audit team performance. Familiarity is examined by comparing senior/manager teams where the managers had either positive prior involvement or no prior involvement with the reviewed seniors. The audit team performance in generating hypotheses in a preliminary analytical review case was measured to assess any differences due to those attributes. Consistent with expectations, I find that post-review discussion and familiarity with the preparers are both, independently, important sources of audit team performance gains in a review process that includes face-to-face discussions.
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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 | 0.030 | 0.368 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".