‘She Ended Up Controlling Every Aspect of My Life’: Male Victims' Narratives of Intimate Partner Abuse Perpetrated by Women
Bibliographic record
Abstract
Abstract Woman's use of violence has been mainly conceptualised through woman's experiences of victimization. However, more recent perspectives emphasise the female agency, responsibility and meaning of woman's violence. Listening to the voices of victims of women's abuse is a powerful way of learning about woman's use of violence and its impact on the victims. We conducted focus groups with 41 men from four countries who experienced female-perpetrated abuse. Four major types of abuse were identified: psychological abuse and coercive control followed by physical violence and sexual violence. Psychological abuse ranged from verbal assaults and gaslighting to provoking physical altercations and reporting false accusations. Patterns of control included deliberate isolation, threatening false accusations and financial domination. Men reported that women initiated physical violence for various reasons, including jealousy and rage. Some women used different objects that could seriously hurt, including knife, while others slapped, bit, punched or kicked. Several men reported female-perpetrated sexual abuse. Woman's use of violence in the intimate relationship should be treated seriously. A more gender-inclusive approach to partner abuse is required that can focus on a better prevention of abuse for all victims.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".