Implications of Divided Responsibility in Audits Involving Component Auditors
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 article summarizes and reflects on the practical implications of the published study “Are Referred-To Auditors Associated with Lower Quality and Efficiency?” (Krishnan and Li 2023). Audits of companies frequently involve the participation of auditors (who audit components of clients) other than the lead auditor that signs the audit report. In general, the work of these component auditors is assimilated in the lead auditor’s report. However, uniquely in the United States, the lead auditor sometimes formally divides responsibility with the component auditor and refers to the component auditor’s work in its audit report. These component auditors are “referred-to” auditors. Krishnan and Li (2023) examine factors associated with the use of referred-to auditors as well as the associations between the use of referred-to auditors and measures of audit quality and audit efficiency.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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