An Exploration of the Victim-Offender Relationship in Sexual Offending: Predicting Severity and the Presence of Violence
Bibliographic record
Abstract
Research indicates that 19% of women and 2% of men will have been raped in their lifetime (The National Center for Victims of Crime 2018). The most recent literature on sexual offenders examines the effectiveness of current sex offender registration and notification laws. However, the bulk of the literature on sexual offending addresses the potential risk factors and predictors associated with the development of sexual offending, with a niche pocket of research exploring the decision-making processes of sex offenders. Only limited research exists with respect to the victim-offender relationship, and no research to date has applied rational choice and deterrence models to the exploration of the victim-offender relationship. Thus, the current study examines 1,758 randomly sampled registered sex offender profiles from the official New York State Sex Offender Registry database in order to determine whether the victim-offender relationship can predict the level of severity and presence of violence in sexual offence cases. While the study yields mixed findings, results open the doors for multiple avenues for future research.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".