“Society Wants to See a True Victim”: Police Interpretations of Victims of Sexual Violence
Bibliographic record
Abstract
Despite attempts to rectify the injustices experienced by victims of sexual violence within the criminal justice system, unfounded rates for sexual violence remain high and many victims continue to feel disempowered and voiceless. In this context, police officers wrestle with how to support victims, while protecting those who may be falsely accused and grappling with deeply imbedded cultural beliefs about who constitutes a “true” victim. In the current article, we draw on interviews with officers working in Internet Child Exploitation, sex crimes, and child abuse units across 10 Canadian police service organizations to understand how police interpret and respond to child, youth, and adult victims of sex crimes. We unpack the range of interpretations of victims, explore if and how interpretations of victims translate into police perceptions of their interactions with victims, and their interpretations of the possible outcomes that can be offered in the investigation. We highlight the difficulties officers encounter as they strive to balance their occupational role with victims’ needs. We argue that police interpretations of sexual violence and sexual violence victims are shaped by the officer’s adherence to or rejection of understandings of the “ideal victim”.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.040 | 0.054 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 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".