Balancing Transparency and Accountability with Privacy in Improving the Police Handling of Sexual Assaults
Bibliographic record
Abstract
This article considers the potential for the adoption in Ontario of a model, developed in Philadelphia and implemented in other US cities, that has proven successful in significantly improving police handling of sexual assault cases and public confidence in the system. This model directly involves front-line sexual assault victim advocates working with police in systematic reviews of police sexual assault records, with a particular focus on “unfounded” cases. Resistance to the adoption of this model in Canada has focused on arguments around public sector privacy legislation. We therefore explore the Philadelphia model through a transparency and accountability lens in the Canadian context. We suggest that the concepts of “transparency” and “accountability” are too often conflated with the disclosure of data or information through access to information channels, and we argue for a more robust understanding of these concepts. We also argue that the conventional access to information model should not be allowed to obstruct meaningful transparency and accountability by using privacy arguments to create barriers to change.
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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 | 0.138 | 0.259 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.007 |
| 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".