Developmental and Psychological Profile of Men Who Have Committed Sadistic Sexual Aggression against Women
Bibliographic record
Abstract
The aims of the current study were twofold: (1) to identify factors that distinguish men who commit sadistic sexual aggression from those who do not; and (2) to investigate the developmental trajectories leading to sexual sadism. The study sample was composed of 206 men who had committed sexual aggression against women (69 men who had committed sadistic sexual aggression; 137 men who had committed nonsadistic sexual aggression), all of whom were incarcerated in Quebec (Canada). The Severe Sexual Sadism Scale (SESAS), an empirically validated instrument, was used to characterize the participants. Bivariate (χ2) analyses were performed. Our results revealed that men who had committed sadistic sexual aggression against women differed from others in several respects, notably developmental (e.g., emotional and physical abuse), psychological (e.g., avoidant and narcissistic personality profiles), sexological (e.g., deviant sexual fantasies), and criminological (e.g., a structured modus operandi, use of a weapon, anal penetration). Structural equation modeling analysis identified a developmental trajectory leading to the commission of sadistic sexual aggression. Theoretical and clinical implications of our results are discussed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".