Bibliographic record
Abstract
Spousal homicide perpetrators are much more likely to be men than women. Accordingly, little research has focused on delineating characteristics of women who have committed spousal homicide. A retrospective clinical review of coroners' files containing all cases of spousal homicide occurring in Quebec over a 20-year period was carried out. A total of 276 spousal homicides occurred between 1991 and 2010, with 42 homicides by female spouses and 234 homicides by male spouses. Differences between homicides committed by female offenders and male offenders are discussed, and findings on spousal homicide committed by women are compared with those of previous studies. Findings regarding offenses perpetrated by females in the context of mental illness, domestic violence, and homicide-suicide are explored. The finding that only 28% of the female offenders in the Quebec sample had previously been subjected to violence by their victim is in contrast to the popular belief and reports that indicate that most female-perpetrated spousal homicide occurs in self-defense or in reaction to long-term abuse. In fact, women rarely gave a warning before killing their mates. Most did not suffer from a mental illness, although one-fifth were acutely intoxicated at the time of the killing. In the vast majority of cases of women who killed their mates, there were very few indicators that might have signaled the risk and helped predict the violent lethal behavior.
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".