Explaining the information systems auditor role in the public sector financial audit
Bibliographic record
Abstract
This paper addresses the research questions, “What is the role of the IS auditor in supporting the financial audit?” and “What key determinants affect that role?” through the development of an explanation theory for the role of the IS auditor in the public sector financial audit. Results are based on semi-structured interviews with 55 senior auditors and IS auditors. These auditors worked in ten practice offices in the Australian, Canadian, New Zealand and United Kingdom public sectors. We manually coded 23 interview transcripts and used the Leximancer tool to extend this coding to the remaining transcripts through automated text analysis. The analysis allowed the identification of relevant “common statements” representing the prominent and shared perceptions of the IS auditor role amongst these auditors. These common statements provided a basis for the development of an initial explanation theory. One new construct presented in this theory is the practice office's “IS audit emphasis”, which represents the practice office's emphasis upon the relationship between the IS auditor role and the audit team. The explanation theory provides a richer description of current audit practice regarding the IS auditor's role in public sector financial audit than currently exists. Consequently, this research provides insights for those involved in the education and training of auditors by developing a foundation for a more complete understanding of the IS auditor role.
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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 | 0.037 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".