The Estimated Propensity to Issue Going Concern Audit Reports and Audit Quality
Bibliographic record
Abstract
Auditors’ propensity to issue Going Concern Audit Reports (GCARs) is one of the proxies often used for audit quality. Although this propensity is a distinguishing characteristic of auditors, it does not indicate quality according to both theory and practice. In theory, higher quality auditors make fewer audit errors; they are more likely to issue GCARs to clients that deserve them and less likely to issue GCARs to clients that do not. Therefore, the propensity itself does not indicate quality. In practice, Public Company Accounting Oversight Board (PCAOB) inspection reports reveal that the GCAR is rarely mentioned as a deficiency, and in the few cases in which it is discussed, the deficiency is attributed to evidence gathering and estimations, rather than to the GCAR decision itself. The theory and practice motivate our study. This article investigates the empirical ability of the GCAR propensity to proxy for audit quality and finds that different samples and different models yield different determinations of auditor quality. Our findings caution against the use of the propensity to issue GCARs as a proxy for audit quality.
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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 | 0.012 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".