Effects of Decomposition and Categorization on Fraud-Risk Assessments
Bibliographic record
Abstract
SUMMARY This study examines two issues related to the decomposition of fraud-risk assessments. First, it investigates whether there is a significant difference in the fraud-risk assessment of auditors who decompose the fraud judgment from that of auditors who merely categorize fraud-risk factors. Second, it examines whether the perceived need to modify the audit plan and the extent of testing in response to the fraud-risk assessment is significantly influenced by the decomposition of the fraud judgment. In an experiment with 60 audit managers, auditors who decomposed fraud-risk judgments have significantly different fraud-risk assessments than those of auditors who simply categorized fraud cues. When management's attitude cues are indicative of a low fraud risk, decomposition auditors are significantly more sensitive to changes in incentive and opportunity cues than categorization auditors. Finally, auditors who decompose fraud-risk assessments perceive a significantly higher need to revise audit plans and to increase the extent of audit testing.
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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 | 0.011 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".