Preliminary insights and analysis into weapon enabled sexual offenders
Bibliographic record
Abstract
Purpose – Weapon use is recognised as a key crime concern in England and Wales but has received relatively little focused research. The purpose of this paper is to examine weapon use by sexual offenders to develop new insights relevant for a police audience. Specifically, to examine the prevalence of weapons within sexual offenders and explore the differences between weapon and non-weapon enabled offenders on a range of characteristics. Design/methodology/approach – A sample of 1,618 single, stranger, solved, serious sexual assaults were provided by the Serious Crime Analysis Section of the Serious Organised Crime Agency. In all, 20 per cent of offenders were weapon enabled. Findings – There were almost no demographic differences between weapon enabled and non-weapon enabled offenders. In terms of the offence itself, there were many significant differences between the groups in terms of precautions used, victim involvement, injury, attack behaviours, victim approach and attack location. Further multivariate analysis revealed aspects of the offence that were associated with weapon use; these are broadly discussed within themes of violence and evidence of planning. Originality/value – The authors argue that an examination of weapon use is valuable in illustrating how offenders differ in their offence and provide insights for the investigation of such crime.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.007 | 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".