Bibliographic record
Abstract
ABSTRACT This paper studies how legal liability due to negligence can weaken or strengthen an auditor's reputation concerns in the client market to provide high audit effort. A negligence liability rule relies on auditing standards to provide a threshold for the level of due care. When the negligence standard is lax, legal liability can weaken the auditor's reputation incentives, with lower audit effort than without legal liability. If the damage payment is low, noncompliance is less costly, because with compliance, reputational concerns cause the auditor to provide higher costly audit effort than the standard. In this case, investors also prefer noncompliance, and earnings quality is lower than if there were no legal liability damages. When the standard is stringent, noncompliance is less costly for the auditor, and legal liability strengthens reputation incentives. Investors may also prefer noncompliance, and earnings quality is higher than if there were no legal liability damages. JEL Classifications: M41; M42; D82; M48.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".