Behavioural Consistency and Offender Characteristics: Investigating Modus Operandi Patterns in Serial Stranger Sex Offences
Bibliographic record
Abstract
Case linkage is a statistical technique which connects multiple sexual assault cases to a single perpetrator and holds promise for informing criminal investigations. Further, examining the behaviours executed most consistently across serial offences committed by a given offender is crucial to linking offences. The current study investigated behavioural consistency in a sample of 49 male serial stranger sexual offenders responsible for 147 offences. For each offence, four crime aspects were identified: 1) pre-crime facilitators, 2) victim selection and characteristics, 3) approach and attack methods, and 4) crime scene characteristics. Consistency between and within each crime series and across offender types based on background characteristics was examined. Results indicated a high degree of behavioural consistency across all crime aspects. Behaviours occurring prior to the offence were particularly useful in establishing consistent offending patterns. The implications of these findings in the context of police investigations and their utility for clinical practice 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".