Conventions of Audit Quality: The Perspective of Public and Private Company Audit Partners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.010 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it