From principle to practice: evaluating the effectiveness of mandatory ethics training on CPA code compliance
Bibliographic record
Abstract
This study examines the impact of mandatory ethics training on the professional behavior of Chartered Professional Accountants (CPAs) in Quebec, where a 4-hour ethics training requirement was introduced in 2018. The importance of ethics education in the accounting profession is highlighted by numerous financial scandals that have underscored the need for continuous ethical vigilance. Our analysis, comparing pre- and post-requirement periods, reveals a significant decrease (31.58%) in the number of infractions, suggesting that mandatory ethics training effectively reduces unethical behavior. This study also finds that the moral intensity of infractions increased post-requirement, suggesting that while the frequency of infractions has decreased, the observed increase in their severity may reflect underlying changes in enforcement, reporting, or disciplinary trends during the same period. This trend suggests that ethics training may deter minor infractions but is less effective in preventing more serious ethical violations, particularly among repeat offenders. The persistence of high moral intensity infractions among this group underscores the need for targeted interventions beyond standard training. These findings suggest that mandatory ethics training may be a useful tool for encouraging ethical behavior but its impact could vary depending on the severity of past misconduct and the professional context.
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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 | 0.071 | 0.337 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".