The Forewarning Effect of Critical Audit Matter Disclosures Involving Measurement Uncertainty*
Bibliographic record
Abstract
ABSTRACT We present experimental evidence suggesting that critical audit matter (CAM) disclosures in the auditor's report involving areas of high measurement uncertainty forewarn users of misstatement risk. Specifically, in our first study with MBA students, financial analysts, and attorneys, we find that CAMs (i) lower premisstatement assessments of confidence in the financial statement area disclosed as a CAM, and (ii) lower assessments of auditor responsibility for a subsequently revealed misstatement in a CAM‐related area. In our second study with student participants proxying as mock jurors, we find that the responsibility‐mitigating effect of CAM disclosure is driven by CAM disclosures involving measurement uncertainty, as opposed to CAM disclosures involving categorical determinations. Combined, our findings help reconcile mixed evidence from prior research, supporting the view that the forewarning effect of CAM disclosures involving measurement uncertainty could mitigate perceived auditor responsibility for CAM‐related material misstatements.
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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 | 0.012 | 0.168 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".