Individual Differences and Rating Errors in First Impressions of Psychopathy
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
The current study is the first to investigate whether individual differences in personality are related to improved first impression accuracy when appraising psychopathy in female offenders from thin-slices of information. The study also investigated the types of errors laypeople make when forming these judgments. Sixty-seven undergraduates assessed 22 offenders on their level of psychopathy, violence, likability, and attractiveness. Psychopathy rating accuracy improved as rater extroversion-sociability and agreeableness increased and when neuroticism and lifestyle and antisocial characteristics decreased. These results suggest that traits associated with nonverbal rating accuracy or social functioning may be important in threat detection. Raters also made errors consistent with error management theory, suggesting that laypeople overappraise danger when rating psychopathy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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