Still waters run deep: self-control as a moderator of dark personality traits for antisocial conduct and violent attitudes
Bibliographic record
Abstract
Although self-control is frequently comorbid with other antisocial features, some individuals who exhibit psychological risk factors for antisocial conduct nevertheless have relatively high self-control. With this complexity in mind, the current study examined self-control as a potential moderator of antisociality/criminality features and violent attitudes using a community sample of 354 adult participants from Portugal. We found significant evidence that self-control moderates the dark core of personality when predicting antisociality/criminality, but not when predicting violent attitudes. We also found self-control moderates psychopathy when predicting antisocialty/criminality, but self-control had more robust moderation effects for violent attitudes. Specifically, self-control moderated narcissism, psychopathy, and sadism. Self-control did not moderate Machiavellianism in either model. Findings corroborate the notion that self-control plays an important role in moderating some dark traits of personality that are significant predictors for antisocial/criminal behaviors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".