Identifying femicide using the United Nations statistical framework: Exploring the feasibility of sex/gender-related motives and indicators to inform prevention
Bibliographic record
Abstract
According to the United Nations Office on Drugs and Crime, 55% of women and girls killed in 2022 died at the hands of intimate partners or family members, contexts indicative of femicide. The proportion of the remaining 45% of women and girls killed which involved sex or gender-related elements remains largely unknown. This is due to the lack of high-quality, gender-sensitive data collection tools and the few systematic efforts to more consistently and accurately document femicide. Information about femicide in marginalized and racialized communities is further affected because many of these deaths remain invisible in official data for women and girls who live – and die – at the intersections of race, poverty, ability, sexuality, and other social identities. Drawing from a recently released international statistical framework for measuring gender-related killings of women and girls, this article examines the presence of sex/gender-related motives and indicators in a Canadian sample, drawing data from publicly available information. Findings about the feasibility of documenting sex/gender-related motives and indicators generally and for specific groups of women and girls are discussed.
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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 | 0.032 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".