Can combining judgment decomposition and notetaking improve group auditors' sensitivity to qualitative risk?
Bibliographic record
Abstract
Abstract In this study, we leverage judgment decomposition and information acquisition theories to develop and test an intervention to improve group auditors' identification of and response to component‐level qualitative risk. Improving group auditors' response to qualitative risk is important because (1) group audits are prevalent today and require multiple qualitative risk assessments, (2) auditors have historically overlooked qualitative risks, and (3) prior interventions have failed to improve auditors' response to qualitative risk. In an experiment with 88 audit partners and managers, we find that a hybrid risk assessment approach that combines elements of judgment decomposition and notetaking improves auditors' group audit planning decisions. Specifically, auditors utilizing our hybrid approach are better able to identify and respond to component‐level qualitative risks than auditors who use a holistic approach. Importantly, the improvement in qualitative risk response does not come at the expense of auditors' response to quantitative risk.
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How this classification was reachedexpand
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".