A Pre-Registered Examination of the Relationship Between Psychopathy, Boredom-Proneness, and University-Level Cheating
Bibliographic record
Abstract
Academic cheating is a prevalent problem in all educational institutions. Finding solutions for cheating requires an understanding of who is more likely to engage in these behaviors. In this pre-registered study (including an a priori power analysis), we investigated the relationship between the four facets of psychopathy, boredom-proneness, and academic cheating in undergraduate university students ( N = 161) while controlling for demographic factors (age, sex, and socioeconomic status) and attitudes supportive of cheating. Students were asked whether they had cheated in the fall 2021 term (yes/no) and about the different types of cheating behaviors they engaged in. Overall, 57% of students admitted to cheating, with online cheating being the most frequently reported behavior. Participants scoring higher on the antisocial facet of psychopathy and endorsing more positive attitudes towards cheating were more likely to report cheating in fall 2021 and engaged in a higher number of different types of cheating behaviors. Those scoring lower on the affective facet of psychopathy (i.e., more emotional) were also more likely to engage in a higher number of cheating behaviors. Boredom-proneness was correlated to both cheating outcomes in the bivariate analyses, but this effect disappeared once controlling for psychopathy and other known correlates. Understanding the features of students who engage in cheating behaviors allows for a critical examination of the potential effectiveness of anti-cheating policies and the development of more preventative classroom practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".