Defining the Heart of Darkness: A Psychometric and Behavioral Analysis of the Relationship Between Psychopathy and Sadism
Bibliographic record
Abstract
Psychopathy and sadism, personality constructs largely characterized by antagonistic tendencies, share several similar traits and behaviors such as cruelty, callousness, and antisocial behavior (Foulkes, 2019; Hare, 2003). They are two of the most severe risk factors for criminal offending and aggression across a wide range of contexts, rendering these traits major public health concerns (Foulkes, 2019; Kiehl & Hoffman, 2011; Reidy et al., 2015). Despite the risks they pose, these constructs are relatively poorly understood. It is unclear how much of psychopathy can be accounted for by sadism and vice versa. It is possible that sadism and psychopathy reflect two distinct constructs, each with its own defining features, factor structures, and nomological networks, which would have important implications for theory, practice, and policy (e.g. risk assessment, parole hearings). Yet, the degree of overlap and distinction between these traits has yet to be empirically and thoroughly examined. The overarching goal of the present dissertation project is to investigate the degree of overlap between psychopathy and sadism across three levels of analysis: psychometric, behavioral, and physiological. This project has the potential to provide valuable insights to theories of antagonistic personality traits and improve clinical and forensic assessment procedures. Foulkes, L. (2019). Sadism: Review of an elusive construct. Personality and Individual Differences, 151, 109500. Hare, R. D. (2003). The Hare psychopathy checklist revised. Toronto, ON, Canada: Multi-Health Systems, Incorporated. Kiehl, K. A., & Hoffman, M. B. (2011). The criminal psychopath: History, neuroscience, treatment, and economics. Jurimetrics, 51, 355. Reidy, D. E., Kearns, M. C., DeGue, S., Lilienfeld, S. O., Massetti, G., & Kiehl, K. A. (2015). Why psychopathy matters: Implications for public health and violence prevention. Aggression and Violent Behavior, 24, 214-225.
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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 | 0.005 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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 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".