Prospective evaluation of a new audit standard: Expert rhetoric and flexibility in cost‐benefit analysis
Bibliographic record
Abstract
Abstract The objective of this research is to better understand experts' contributions to the prospective evaluation of a new audit standard—in this case, key audit matter (KAM) reporting. To this end, we assisted the Canadian Auditing and Assurance Standards Board by leading its consultation of 22 expert financial statement users. The methodology employed to observe our participants' opinions and cognitive processes involves thought protocol and interviews. By analyzing the rhetorical base of experts' prospective analysis, we show that our participants' arguments are often laden with postulates and lack data points, leading to generalizations. Sounder arguments entail more nuanced views but lead to uncertainties. We therefore highlight a tension between the rhetorical content of experts' insights and the calculative rationality of a cost‐benefit analysis. We also find that experts with less cognitive flexibility are less likely to be supportive of the adoption of a standard implying a change of habits in the way they process information. This tension and cognitive bias generate a significant interpretive challenge to determine a clear and dominant stance in the consultation. We discuss the implications of these findings for the legitimacy of prospective evaluations and the conduct of cost‐benefit consultations with experts. We also contribute to the literature on KAMs by substantiating concerns about the value of extended auditor reports to users.
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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 | 0.227 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".