Effects of Decomposition and Categorization on Fraud-Risk Assessments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it