The Subjective Experience of Men Who Murder Their Intimate Partners
Bibliographic record
Abstract
In Canada approximately 70 women die each year at the hands of someone with whom each of them was or had been in an intimate relationship. This statistic has remained relatively static over a twenty year period of time. This study explores the subjective experiences of men who killed their intimate partners with the expressed purpose of examining ways of addressing this loss of life. There is a dearth of social work research and literature on femicide. What research there is borrows theories from psychology, women’s studies, sociology and criminology. The primary theory used is feminist theory and this theory suggests that men who kill their intimates are the same men who abuse their partners and that murder is a crime of power and control This theory contends that men have been socialized into a patriarchal society which allows them to have power and control over women. Several researchers who do not use a feminist lens to examine the issue suggest that feminist theory is too narrow and simplistic to explore this complex issue. This researcher also found this to be the case. Data from six in-depth interviews with men who murdered their intimate partners reveal that contrary to feeling powerful these informants felt powerless against the increasing emotion they were experiencing when faced with real or imagined destruction or loss of the relationship. The data further reveal that the respondents in this study came from family backgrounds which were abusive to varying degrees. Suggestions for further research as well as policy and practice implications are examined.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".