Escalation from Fetish Burglaries to Sexual Violence: A Retrospective Case Study of Former Col., D. Russell Williams
Bibliographic record
Abstract
Abstract Criminal history narrative studies reveal an escalation in sexual offender behaviour from non‐contact to contact offending, with an ever‐increasing likelihood of sexual violence and homicide. In particular, researchers have found that sexual offenders often have a history of committing burglaries prior to contact offences. Accordingly, researchers have suggested that burglaries may be associated with an increased likelihood of future sexual offending, particularly when they have a sexual element to them. However, to date, there has been little quantitative research focusing on the mechanisms of escalation in sexual offences. This paper seeks to study factors associated with sexual offence escalation in terms of changes in offence seriousness and frequency. Specifically, case evidence was gleaned from a series of fetish burglaries and subsequent sexual assaults and murders committed by the former Canadian Colonel David Russell Williams (RW). Cluster analysis, chi‐square, ANOVA, and regression analyses were conducted on the crime scene information of RW's 82 cases of fetish burglary. Analyses revealed a significant escalation in the frequency and seriousness of RW's fetish burglary offences prior to committing acts of sexual violence and ultimately sexual homicide. Recommendations for future research predicting escalation of sexual offending by frequency and seriousness of offending behaviour are discussed. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".