Punishing femicide: Criminal justice responses to the killing of women over four decades
Bibliographic record
Abstract
The way in which nation states respond to femicide has become the focus of much attention in the past decade. The establishment of specialized police and prosecution units has been recommended and some countries have implemented specific legislation or criminal offences specific to femicide. Part of the challenge in moving beyond these legislative and policy initiatives is the dearth of reliable data that show how states are actually punishing crimes of femicide on the ground. Using data that document punishment outcomes in cases of femicide over four decades in Canada’s most populous province, this article examines how punishments compare for female and male homicide victims, across femicide subtypes and over time. Results show that cases involving female victims attract more punitive court responses overall than cases with male victims. Second, intimate and familial femicides are treated more leniently at several stages than other femicides. Finally, there have been positive changes in the punishment of femicide over time, paralleling legislative and policy responses to violence against women in Canada. Priorities for future research that address the role played by dominant stereotypes in punishment related to particular types of femicide as well as some women’s increased risk are highlighted.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".