Identifying femicide using the United Nations statistical framework: Exploring the feasibility of sex/gender-related motives and indicators to inform prevention
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it