Withholding homicide victim names: Looking for a win-win solution for families and the police
Bibliographic record
Abstract
Although withholding the names of homicide victims from the public is a relatively new police practice, it has proven to be controversial, with the media, legal scholars, and victim advocacy groups often opposing these policies. In order to better understand the issue of withholding names, we examined the prevalence of these practices in Canada’s largest municipal police services. These results were further explored in a series of semi-structured interviews with stakeholders from 20 victim services and advocacy organizations. Analysis of the interview and survey results reveal that the key priority of the police is maintaining the integrity of their investigations, and all other issues are secondary. Although the issue of withholding information has become contentious, many of the arguments become moot, as the friends and family members of these victims often post the information related to these deaths on social media, effectively bypassing both the press and the police. Implications for policy development are discussed in light of these findings.
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.039 | 0.020 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".