The Effects of Accounting Standard Precision, Auditor Task Expertise, and Judgment Frameworks on Audit Firm Litigation Exposure
Bibliographic record
Abstract
SUMMARY This article summarizes the published study “The Effects of Accounting Standard Precision, Auditor Task Expertise, and Judgment Frameworks on Audit Firm Litigation Exposure” (Grenier, Pomeroy, and Stern 2015), where the authors examine ways that auditors can defend their judgment during litigation regarding the appropriateness of clients' application of imprecise accounting standards. The authors find that utilizing technical experts will reduce litigation exposure arising from imprecise accounting standards because it is difficult to challenge judgments made by a recognized expert. However, the study also finds that using a framework for making high-quality professional judgments represents a cost-effective alternative to technical expertise, as doing so also constrains jurors' ability to challenge auditors' judgments. In sum, the study suggests that auditors are well equipped to handle the increased litigation exposure associated with imprecise accounting standards, and the ongoing worldwide transition to such standards is unlikely to lead to auditor herding to industry norms.
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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 | 0.028 | 0.261 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".