Trust in Police, Victim Blame Attitudes, and Lived Experience with Violence as Predictors of Perceptions of Clare’s Law in a Canadian Sample
Bibliographic record
Abstract
The prevalence of domestic violence (DV) in Canada has prompted federal and provincial consideration and implementation of Clare’s Law (CL) in various regions throughout Canada. CL is legislation that allows people in intimate relationships to request information from the police about their partner’s history of DV and risk for future violence. The purpose of sharing this information is to aid people in making informed decisions about their relationships, with the hope of reducing DV. However, there is little empirical research on CL and no one knows whether the framework can prevent incidents of DV, especially for some populations (e.g., younger women, Indigenous peoples, LGBTQ+) who are higher risk and face additional barriers to reporting or seeking help. Despite being in effect in several Canadian provinces, little is known about Canadians’ knowledge of CL or their perceived likelihood of using it. This research uses Amazon Cloud Research to survey Canadians’ perceptions of CL. We will test whether prior experiences of DV, trust in police, and fear of victim-blaming predict Canadians’ support of CL and their willingness to use CL in the future. We will explore potential barriers to using CL (e.g., safety concerns) and discuss implications for the accessibility and perceived utility of this controversial new 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".