Testing the Behavioural and Environmental Consistency of Serial Sex Offenders: A Signature Approach
Bibliographic record
Abstract
Abstract The present study examines consistency of crime behaviour among 347 sexual assaults committed by 69 serial sex offenders. This individual behaviour approach—the so‐called signature approach—reveals which features of crime behaviour are consistent across a series and which features are not. The consistency scores were calculated using the Jaccard's coefficient. The results of this study indicate that there are some crime features of a serial sexual assault that can be useful for the purpose of linkage. Another important finding is that consistency scores for different variables within the same category can differ substantially. Moreover, serial sex offenders are more likely to be consistent in their environmental crime features when they are also consistent in their behavioural features, and vice versa. Serial sex offenders are also more likely to be consistent in the behavioural features of their assaults as the crime series gets longer. The implications of the results are discussed in relation to both research and practise. Copyright © 2012 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".