The Joint Effects of Multiple Legal System Characteristics on Auditing Standards and Auditor Behavior
Bibliographic record
Abstract
Abstract This paper derives the impacts of legal system characteristics and auditing standards on auditor behavior (audit quality), and analyzes the determination of optimal auditing standards under different legal regimes. Legal regimes are characterized by differences in the uncertainty concerning the outcome of legal proceedings (termed vagueness of legal systems) and differences in the average size of damage awards. Auditing standards as determined by standard setters can vary in both toughness and vagueness. Our analysis provides implications for the adoption of International Standards on Auditing ( ISA ). Countries, such as the United States, where auditor legal liability is significantly more onerous than the global norm are not likely to adopt ISA , since these standards may not induce auditors to provide the optimal level of audit quality. Conversely, the adoption of ISA by countries, such as China, where the legal system makes the recovery of damages from auditors quite difficult, is not by itself likely to result in a high level of audit quality. Furthermore, our model suggests that auditor rotation can help improve audit quality, but only in certain circumstances.
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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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".