Technology and Its Implications for Staff Auditors
Bibliographic record
Abstract
SYNOPSIS To increase audit quality and decrease costs, audit firms have adopted technology to reduce routine tasks and increase the sophistication of data analysis. As a result, the Staff Auditors (SA) function has undergone a major shift requiring SA to be involved in more complex tasks involving higher level analysis, judgment, and greater technical skills. In addition, because technology has reduced the size, composition, and duration of time which the audit team spends at the client premises, the traditional on-the-job learning model of SA involving extensive interaction with senior audit team members has been altered. SA are by default becoming the face of the auditor at the client’s premises. Firms and educators need to rethink the nature of the training of SA and address the education and skill set of the students entering the audit profession. We discuss the implications, opportunities, and challenges of these changes for both firms and educators. JEL Classifications: M42; M40.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".