“This isn’t your father’s police force”: Digital evidence in sexual assault investigations
Bibliographic record
Abstract
Digital evidence, once regarded as existing only in a portion of criminal cases, in our digitized world commonly appears within all crime categories and is a factor in many (or arguably most) cases of sexual assault. In this article, we draw from 70 interviews with sex crime investigators from across Canada to demonstrate that the infusion of digital evidence into sexual assault investigations results in new opportunities and challenges for police and both negative and positive impacts on victims’ experiences within the criminal justice system. We show that while digital evidence certainly provides more opportunities for documenting the context and content of acts of sexual assault, police perceive this evidence as a double-edged sword that provides both more evidence and new challenges for police and victims. While officers express that digital evidence may provide more conclusive proof in the notoriously difficult pursuit of proving sexual assault charges, they are also concerned that this evidence provides new challenges for already overburdened sex crime units and makes cases more lengthy and invasive for victims. This article contributes to emerging research on the challenges of policing in the digital age and to the dearth of research on the potential and pitfalls of digital evidence in sexual assault investigations.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".