Why Doesn't She Just Report It? Apprehensions and Contradictions for Women Who Report Sexual Violence to the Police
Bibliographic record
Abstract
A specific goal of law and policy reform has been to encourage women to come forward with sexual assault complaints, but has the nature of the police response improved to warrant this encouragement? While analyses of attrition point to important junctures where cases are dropped, less is known about the diverse and complex decisions women make to engage the criminal justice system and the apprehensions and contradictions that play out in their dealings with the police. This article presents the results of a study of sexual assault survivors whose assaults were reported to the police in a mid-sized Canadian city through the analysis of their experiences, from the decision to report to the police through to their interactions with front-line officers and sexual assault investigators. While some police officers delivered procedural justice in the form of a professional non-judgmental response, others acted on “real rape” understandings of sexual assault and conveyed disbelief, scepticism, and a poor understanding of the effects of trauma. Although charging and prosecution rates have not improved, results of this study show that survivors who engage with police are increasingly likely to expect a positive response. Some women were willing to trust that “things have changed” or their experience was unique. In an era of growing formal equality and heightened expectations of police, results of this study show that there is a long way to go before women are guaranteed equality in the application of sexual assault law.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".