A Case of Academic Misconduct: Does Self‐Interest Rule?*
Bibliographic record
Abstract
Abstract Most analyses of academic misconduct focus on students’ integrity and what is taught at the universities. Surprisingly little attention is paid to the role of faculty members. This article presents an unusual case of academic misconduct that provides an opportunity to examine the actions and rationalizations of the students and faculty members involved in the event as well as the broader university context. The case is unusual in that the instructor initiated and facilitated the academic misconduct. The analysis of the misconduct and the subsequent events suggest that self‐interest rules and concerns for wider interests are all but silent. While the case presents a somewhat dismal view of the integrity of some accounting faculty members and future accountants, it provides interesting insight into self‐interest, rationalization, social context, and both students’ and faculty members’ integrity. The analysis discusses the mechanisms used to prevent and manage faculty member misconduct, along with limitations of self‐regulation and student reports as forms of control. The article also considers how accounting educators can encourage future accountants to act with integrity and concludes that in order to achieve that goal, accounting educators must serve as role models who act honestly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.014 | 0.017 |
| 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".