Auditors' and Specialists' Views About the Use of Specialists During an Audit
Bibliographic record
Abstract
ABSTRACT Auditors often rely on the assistance of specialists from such fields as tax, information technology, valuation, and forensic accounting. Integration of the work of specialists with the work of audit team members is a challenge for both groups. This interview-based study of 34 practitioners from six accounting firms, including 12 auditors (partners and managers) and 22 specialists (tax, IT, valuation, forensic) examines auditors' and specialists' views about the current state of specialist use on audits. The regulatory environment creates pressure for financial statement auditors to use specialists on audits; however, financial statement auditors often seek to limit specialist involvement. Both auditors and specialists are dissatisfied with the current situation, but for different reasons. Auditors are concerned about budget overruns, delays, and harm to client relationships by (overly) meticulous specialists. Specialists are concerned about auditors limiting the scope of specialist involvement, and its effect on audit quality. JEL Classifications: M4; M40; M42.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".