From Plagiarism‐Plagued to Plagiarism‐Proof: Using Anonymized Case Assignments in Intermediate Accounting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Plagiarism in accounting case assignments is a serious problem that undercuts the important objectives the case assignments are used to achieve: the development of students’ critical thinking skills and the advancement of their written communication skills. This paper examines plagiarism behavior through the lens of fraud theory by targeting two elements of the fraud triangle: rationalization and opportunity. Efforts to target rationalization by changing student perceptions of peer behavior were not effective. This led to a shift in focus to the assignment itself which was providing the opportunity for misconduct. Students were sidestepping authentic engagement in the assignment by gaining access to published solutions or peer submissions from previous semesters. One failsafe design response to this problem is to use only originally written cases. However, writing a large number of original cases each semester is unrealistic within many instructors’ workloads. This article instead proposes the use of a case refreshing strategy that makes cases appear unique to each group of students. When this strategy was introduced in an intermediate accounting course, there was a dramatic decrease in plagiarism and a corresponding improvement of the academic integrity environment in the course.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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 it