Femicide in Canada
Bibliographic record
Abstract
Every second day, on average, a woman or girl is killed in Canada. These women and girls are killed mostly by men. This chapter examines how these deaths are studied with a focus on the research produced by the Canadian Femicide Observatory for Justice and Accountability (CFOJA), launched in 2017. Consistent and reliable data about the killings of women and girls who were killed because of their sex or gender, known as femicide in the literature, is limited globally. Information about femicide in marginalised and racialised communities is even more lacking. Noting that existing official data-collection instruments are primarily designed to capture information about homicides, which are typically male-on-male instances of violence, the authors argue that femicide has specific gendered characteristics and that understanding these characteristics can help design prevention initiatives. In other words, women and girls remain at risk of femicide due to the lack of high-quality gender-sensitive data-collection tools, including race-based data. Despite the data limitations, CFOJA produces research on femicide drawing from media accounts and publicly available information on femicides, which are often enough to identify and discuss sex-/gender-related motives and indicators (SGRMIs) while continuing to engage in public advocacy about femicide. The chapter presents a snapshot of femicide in Canada and discusses the presence of SGRMIs, concluding that femicide is a public safety and human rights issue concerning all members of society and should be studied accordingly.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".