Identifying and profiling scholastic cheaters: Their personality, cognitive ability, and motivation.
Bibliographic record
Abstract
Despite much research, skepticism remains over the possibility of profiling scholastic cheaters. However, several relevant predictor variables and newer diagnostic tools have been overlooked. We remedy this deficit with a series of three studies. Study 1 was a large-scale survey of a broad range of personality predictors of self-reported cheating. Significant predictors included the Dark Triad (Machiavellianism, narcissism, psychopathy) as well as low agreeableness and low conscientiousness. Only psychopathy remained significant in a multiple regression. Study 2 replicated this pattern using a naturalistic, behavioral indicator of cheating, namely, plagiarism as indexed by the Internet service Turn-It-In. Poor verbal ability was also an independent predictor. Study 3 examined possible motivational mediators of the association between psychopathy and cheating. Unrestrained achievement and moral inhibition were successful mediators whereas fear of punishment was not. Practical 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 | 0.002 | 0.008 |
| 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 | 0.000 | 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".