Dark Triad Traits and Cyberbullying Perpetration: Addressing Current Limitations in Dark Triad Studies
Bibliographic record
Abstract
Cyberbullying remains a recurring problem in the digital age. Individual differences in Dark Triad personality traits (i.e., psychopathy, narcissism, Machiavellianism) have emerged as consistent predictors of cyberbullying. However, these findings may be affected by methodological limitations, including statistical partialing and the use of short, unidimensional, scales that conflate psychopathy and Machiavellianism. This study examined associations between cyberbullying perpetration and facets of psychopathy, Machiavellianism, and narcissism, while addressing the methodological shortcomings of previous studies. Canadian adults (N = 1,725) completed items pertaining to cyberbullying perpetration, along with the Self-Report Psychopathy Scale short form (SRP-4-SF), the Five Factor Machiavellianism Inventory (FFMI), the Narcissistic Grandiosity Scale (NGS), and the Narcissistic Vulnerability Scale (NVS). Confirmatory factor analysis supported the proposed structure of the SRP-4-SF, NGS, and NVS, but not the FFMI. Separate structural equation models were computed to estimate the association between each antagonistic trait and cyberbullying perpetration, controlling for age and sex. The antisocial facet of psychopathy and grandiose and vulnerable narcissism were significant positive predictors of cyberbullying perpetration. Cyberbullying prevention may be improved by designing interventions that account for the antisocial and narcissistic tendencies of cyberbullies. Focusing future research on narcissism and psychopathy would allow for greater scientific consilience within personality psychology.
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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 | 0.088 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".