Auditors’ Perceptions of Responsibilities to Detect and Report Client Illegal Acts in Canada and the UK: A Comparative Experiment
Bibliographic record
Abstract
This paper investigates the theme of the role and influence of auditing standards on auditing practice by means of a comparative case study focused on two different standard setting regimes. Specifically, the Canadian and UK standards concerned with the detection and reporting of client illegal acts are considered. Fifteen short vignettes are developed each describing an illegal act and reflecting the distinctive factors highlighted by the respective auditing standards. We find that the regulation of the auditing profession through the mode of auditing standards does have an identifiable impact on auditor behavior. The impact of standards on auditors’ perceptions of their responsibilities in the area of illegal acts is clearer for detection than it is for reporting. Additionally, auditors continue to perceive a clear distinction between fraud and other illegal acts and recognize a higher degree of res‐ponsibility for illegal acts involving fraud than for others. At the other end of the spectrum auditors do recognize some degree of responsibility in connection with illegalities that do not fall within the ambit of auditing standards. Auditors are also influenced in their judgments on the general nature of an illegal act rather than on how the illegality fits the framework adopted by the appropriate auditing standard.
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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 | 0.016 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".