Student and Practitioner Cheating: A Crisis for the Accounting Profession
Bibliographic record
Abstract
In this essay, we propose that the prevalence of cheating by accounting students and serial cheating by accounting practitioners at Big-4 accounting firms are related. Our model of this problem suggests that students who cheat in school become practitioners who cheat in practice, and practitioners, in turn, model dishonest behavior for students. We propose that this vicious cycle of dishonesty poses a threat to the public’s trust in the accounting profession, and this crisis calls for drastic measures, both in academia and in practice, akin to measures like the Sarbanes–Oxley Act of 2002. As an honorable profession, dishonesty cannot be tolerated. Brief overviews of the prevalence of cheating, both by students and by Big-4 accounting practitioners are presented. Suggestions are included for a three-prong approach by accounting stakeholders to reduce this egregious ethical problem—a problem that, we suggest, is causing a new crisis in confidence for the accounting profession.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".