The role of psychopathy in scholastic cheating: self-report and objective measures
Bibliographic record
Abstract
Despite a wealth of studies, no consistent personality predictors of scholastic cheating have been identified. However, several highly-relevant variables have been overlooked. I address this void with a series of three studies. Study 1 was a large-scale survey of a broad range of personality predictors of self-reported scholastic cheating. The significant predictors were psychopathy, Machiavellianism, narcissism, low Agreeableness and low Conscientiousness. However, only psychopathy remained significant in a multiple regression. Study 2 replicated this pattern using a naturalistic, behavioural indicator of cheating -- plagiarism as indexed by the internet service Turn-It-In. The psychopathy association still held up after controlling for intelligence. Finally, Study 3 examined possible motivational mediators of the association between psychopathy and cheating. Unmitigated achievement and moral inhibition were successful mediators whereas fear of punishment was not. Implications for researchers and educators are discussed.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".