Conventions of Audit Quality: The Perspective of Public and Private Company Audit Partners
Bibliographic record
Abstract
SUMMARY This research is based on an in-depth analysis of 34 interviews with partners in Big 4, medium-sized, and small audit firms that specialize in private and/or public company audits, to explore how they understand the concept of audit quality. Two contrasting conventions—i.e., shared judgment norms—of audit quality emerge from the analysis. Public company audit partners in Big 4 firms espouse what we call the “model” audit quality convention, which considers that audit quality results from a technically flawless audit, where professional judgment is highly formalized, and quality is attested by a perfectly documented audit file that passes Canadian Public Accountability Board (CPAB) and PCAOB inspections. In contrast, partners working primarily on private company audits, regardless of their firm's size, endorse what we call the “value-added” audit quality convention, which considers that audit quality results from tailoring the audit to meet the client's unique needs, where professional judgment is unconstrained, and where quality is attested by the client's perception that the audit has given a better understanding of their financial situation and the associated risks and opportunities. Our analysis also reveals significant tensions within each of these two conventions, and a fear that the current regulatory framework for quality control might end up severely hurting 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 | 0.042 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".