Risk Assessment and Criminogenic Needs Based on Sexual Assault Typologies
Bibliographic record
Abstract
Theoretical and empirically-based typologies for criminal behaviour, including sexual assault, are typically based on the premise that those who commit sexual assault have different individual characteristics. Greater knowledge to identify diverse groups of these individuals may help to understand the criminogenic needs that contribute to their offending and how they can be accurately assessed for risk of reoffending. The current study examines the typology proposed by Knight and Prentky (1990). The Massachusetts Treatment Center Rapist Typology, Version 3 (MTC: R3) identifies five general categories of rapists. These categories include opportunistic, pervasively angry, vindictive, sexual, and sadistic types. Using a sample of 300 individuals who have been investigated for sexual assault, the current study will categorize these individuals into one of these typologies. This research will identify potentially unique criminogenic needs and the predictive validity of validated risk assessment tools for each subtype. It is predicted that these subtypes will present with different criminogenic needs and validated risk tools that assess for recidivism risk will have better accuracy depending on typology. The practical implications of this study will allow criminal justice professionals, such as police, to better assess risk and address the criminogenic needs of individuals who have committed sexual offences based on typology. Department: Psychology Faculty Mentor: Dr. Sandy Jung
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".