Auditors, Specialists, and Professional Jurisdiction in Audits of Fair Values
Bibliographic record
Abstract
ABSTRACT Auditors frequently use valuation specialists to help them evaluate fair values, but researchers and regulators know little about how auditors use these specialists. Based on interviews with 28 auditors and 14 valuation specialists, I develop a theoretical framework informed by expert systems and professional competition theories. The interviews suggest that institutional pressures in the fair value environment unevenly impact auditors and specialists, causing tension between auditors' needs for ontological security and jurisdictional claims. This tension leads to one‐sided competition between auditors and specialists and incomplete acceptance of specialists' work. Auditors' competitive behaviors coupled with this incomplete acceptance result in a tendency to make specialists' work conform to auditors' views. Collectively, these findings suggest that auditors use specialists as an institutional mechanism to create comfort, but not insight. This study links expert systems and professional competition theories, and it provides critical insight into some assumptions underlying tenets of each theory. It also informs researchers, regulators, and practitioners interested in understanding and addressing problems related to the use of specialists.
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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 | 0.041 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".