Understanding sexual violence and factors related to police outcomes
Bibliographic record
Abstract
In the year ending March 2020, an estimated 773,000 people in England and Wales were sexually assaulted. These types of crimes have lasting effects on victims' mental health, including depression, anxiety, and post-traumatic stress disorder. There is a large body of literature which identifies several factors associated with the likelihood of the victim reporting a sexual assault to the police, and these differences may be due to rape myth stereotypes which perpetuate the belief that rape is only "real" under certain conditions. Less is known, however, about the effect these rape myths and stereotypes have on the investigation process itself and the subsequent police outcomes assigned to sex offences. This study aimed to address this gap, providing a profile of all RASSO (rape and serious sexual offences) committed over a 3-year period in one English police force, the police outcomes of these offences, and whether any offences, suspect, or victim variables were associated with different outcomes, in particular the decision to charge or cases where victims decline to prosecute. In line with previous research, the majority of victims were female while the majority of suspects were male, and the most frequent victim-suspect relationship was acquaintance, followed by partner/ex-partner. Charge outcomes were more likely in SSOs and less in rape offences, more likely with stranger offences and less likely than offences committed by partners/ex-partners and relatives, and some non-white suspects were more likely to be charged than suspects of other ethnicities, including white suspects. Victim attrition was more likely in cases where the suspect was a partner or ex-partner and least likely where the suspect was a stranger, more likely in SSOs than in rape cases, and more likely when the victim ethnicity was "other". Law enforcement should be aware of the potential biases, both relating to rape myths and stereotypes and to the biased treatment of victims and suspects based on demographic characteristics, and work to eliminate these to ensure a fairer and more effective RASSO investigative process.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".