Unravelling Crime Series Patterns amongst Serial Sex Offenders: Duration, Frequency, and Environmental Consistency
Bibliographic record
Abstract
Abstract Crime linkage and the investigation of behavioural consistency amongst serial offenders has been a flourishing field of research over the past decade or so, especially with respect to serial sex offenders. The emerging research in this field has often portrayed serial sex offenders as a single, distinct, and homogeneous group. Such an assumption, however, has never been empirically examined. Using a criminal career approach and a sample of 72 serial sex offenders who have committed a total of 361 sexual assaults on stranger victims, the current study aims to examine and describe subgroups of crime series patterns amongst serial sex offenders in terms of duration and frequency of offending. The level of environmental consistency display (i.e. offender's choice of crime location and characteristics of the crime site selected) across subgroups of crime series patterns is also examined. Study findings suggest the presence and heterogeneity of crime series patterns amongst serial sex offenders, advocating for the consideration of subgroups of crime series patterns when studying serial sex offenders. Moreover, the offenders' level of environmental consistency varies across the different crime series patterns identified, allowing for the identification of subgroups of offenders showing a higher or lower level of environmental consistency. Copyright © 2014 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".