Dark or disturbed?: Predicting aggression from the Dark Tetrad and schizotypy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research on the personality foundations of aggression typically implicates either (a) aspects of the so-called "Dark Tetrad" or (b) severe mental disturbance (psychosis). The appearance of psychotic symptoms in general populations is termed schizotypy. We conducted two studies to compare the effects of dark personalities and schizotypy on aggression. Study 1 used standard inventories to investigate the overlap of Dark Tetrad traits with schizotypy in a sample of 977 undergraduates. All tetrad traits except narcissism were positively associated with schizotypy, but only at moderate levels. Study 2 administered the same personality battery to 303 members of an online community sample: Aggression outcomes were measured with both self-reports and a behavioral measure-the Voodoo Doll Task. Regression analyses determined the unique contributions of the five personality variables. Two dark traits-psychopathy and sadism-were strong predictors of self-report aggression. Schizotypy added incrementally to the Dark Tetrad in predicting both self-report and behaviorally measured aggression.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it