Duplicity among the dark triad: Three faces of deceit.
Bibliographic record
Abstract
Although all 3 of the Dark Triad members are predisposed to engage in exploitative interpersonal behavior, their motivations and tactics vary. Here we explore their distinctive dynamics with 5 behavioral studies of dishonesty (total N = 1,750). All 3 traits predicted cheating on a coin-flipping task when there was little risk of being caught (Study 1). Only psychopathy predicted cheating when punishment was a serious risk (Study 2). Machiavellian individuals also cheated under high risk-but only if they were ego-depleted (Study 3). Both psychopathy and Machiavellianism predicted cheating when it required an intentional lie (Study 4). Finally, those high in narcissism showed the highest levels of self-deceptive bias (Study 5). In sum, duplicitous behavior is far from uniform across the Dark Triad members. The frequency and nature of their dishonesty is moderated by 3 contextual factors: level of risk, ego depletion, and target of deception. This evidence for distinctive forms of duplicity helps clarify differences among the Dark Triad members as well as illuminating different shades of dishonesty. (PsycINFO Database Record
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".