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 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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".