On the Use of Checklists in Auditing: A Commentary
Bibliographic record
Abstract
Experimental studies concerning fraud (or “red flag”) checklists often are interpreted as providing evidence that checklists are dysfunctional because their use yields results inferior to unaided judgments (Hogan et al. 2008). However, some of the criticisms leveled against checklists are directed at generic checklists applied by individual auditors who combine the cues using their own judgment. Based on a review and synthesis of the literature on the use of checklists in auditing and other fields, we offer a framework for effective use of checklists that incorporates the nature of the audit task, checklist design, checklist application, and contextual factors. Our analysis of checklist research in auditing suggests that improvements to checklist design and to checklist application methods can make checklists more effective. In particular, with regard to fraud risk assessments, customizing checklists to fit both client circumstances and the characteristics of the fraud risk assessment task, along with auditor reliance on formal cue combination models, rather than on judgmental cue combination, could make fraud checklists more effective than extant research implies.
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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 | 0.081 | 0.430 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.050 | 0.058 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".