Federal judge ideology and the going‐concern reporting incentives of Big 4 and non–Big 4 auditors
Bibliographic record
Abstract
Abstract We analyze whether and how the perceived federal‐level legal liability linked to federal judge ideology is associated with the likelihood of firms receiving going‐concern modified audit opinions and analyze the differential effects on Big 4 and non–Big 4 auditors. We find that Big 4 and non–Big 4 auditors converge in their going‐concern reporting decisions in circuits with more liberal judges. This convergence is caused by the greater effect of judge ideology on non–Big 4 auditors. Furthermore, we empirically examine the association between federal judge ideology and actual lawsuits against auditors and find that judge ideology has a greater impact on lawsuit likelihood for non–Big 4 auditors for the restating companies. When auditors are sued, both the payout likelihood and amount are greater in circuits with more liberal judges, with the effect being more pronounced for non–Big 4 auditors. This study provides evidence on how the perceived exposure to a gross negligence legal standard shapes auditors' going‐concern reporting incentives for the two tiers of auditors in the market. It also adds to the literature on auditor litigation.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".