The Group Nature of Academic Dishonesty & Diffusion of Responsibility in Online Student Chat Groups
Bibliographic record
Abstract
Opportunities for academic dishonesty have changed since the COVID-19 pandemic, as courses moved to virtual formats and online chat groups became an essential means of communication. Prior explanations of academic dishonesty tend to overlook the fact that it is often committed in groups, discounting the role that group based mechanisms play in facilitating this form of deviance. The current study integrates group dynamics into an explanation of academic dishonesty in online student chat groups with a specific consideration of assessing group size and the role of diffusion of responsibility. Using hypothetical vignettes administered to a sample of university students, findings suggest that the involvement of others contributes to an individual’s willingness to participate in academic dishonesty; however, the size of the group is not related to the decision to engage and does not diffuse responsibility for participation. In total, the results affirm the importance of considering the group context but raise additional questions regarding why groups serve as an important inducement to engage in academic dishonesty.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".