Auditors' and Specialists' Views About the Use of Specialists During an Audit
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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