Collaborative Research into International Perspectives on Best Practice in Sexual Offence Investigations
Bibliographic record
Abstract
The number of incidents of sexual offences recorded by the police in England and Wales increased by 12%, when compared with the previous year, up to 116,012 reports in the year ending December 2016. The number of complainants reporting sexual offences, or accessing services designed to support victims of these types of offences, is known to represent only a very small proportion of the true number of victims. \nWhilst physical evidence is only one type of forensic evidence used in sexual offence investigations, it can be incredibly useful in identifying the perpetrator(s) of the crime. Therefore, it is imperative that the quality of any physical evidence is beyond reproach in order to maximise the potential for achieving a positive outcome. The equipment used in evidence collection, and the mechanisms by which any samples are recovered should be regularly reviewed in order to ensure that they remain fit for purpose. \nThis research has attempted to evaluate different stages of sexual offence investigations, from reporting of the offence to appropriate organisations through to the examination of the complainant in order to recover physical evidence. It has involved working alongside colleagues in Psychology and Health, as well as collaborating with external organisations such as Sexual Assault Research Centres (SARCs) and Police Forces. \nFurthermore, this research, supported by the Winston Churchill Memorial Trust, aims to examine practices in the UK, the USA and Canada to gain a better understanding of best practice and to establish mechanisms for continual sharing of this practice in the future. \nFuture plans for development of this research include setting up a ‘Sexual Offences Research Group’ with representatives across the University, as well as key partner organisations, to develop a reputation as leading researchers into the criminal, health and societal impacts of sexual offences.
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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 | 0.318 | 0.322 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.016 | 0.054 |
| Scholarly communication | 0.049 | 0.032 |
| Open science | 0.007 | 0.039 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".