Awareness of <scp>SEC</scp> Enforcement and Auditor Reporting Decisions
Bibliographic record
Abstract
Abstract We find that non‐Big 4 audit offices with greater awareness of SEC enforcement are more likely to issue first‐time going‐concern reports to distressed clients; where SEC “awareness” is measured using (i) audit office proximity to SEC regional offices, and (ii) proximity to specific SEC enforcement actions against auditors. We also show that these non‐Big 4 audit offices issue more going‐concern opinions to clients who do not subsequently fail, indicating a conservative bias that reduces the informativeness of audit reports. This conservative reporting bias is also associated with higher audit fees and higher auditor switching rates. These findings are important because non‐Big 4 firms now audit 39 percent of SEC registrants and issue 88 percent of going‐concern audit reports. For Big 4 offices, we find some evidence that awareness of SEC enforcement may improve reporting accuracy by reducing Type II errors (failing to issue a going‐concern report to a company that fails), although the number of cases is small.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".