“How Can I Be a Victim When I Have children?” Abused Men’s Perceptions of Their Children’s Exposure to Domestic Violence
Bibliographic record
Abstract
A growing body of research finds many men are victims of intimate partner violence (IPV), but there is limited research on children’s exposure to domestic violence (CEDV) in relationships where fathers are the victims of IPV. We focus on fathers’ perceptions of CEDV in the narratives of 30 men who experienced IPV and who are from four English-speaking countries. Four main themes were identified across the countries: children as victims of abuse; effect of abuse on children; the men’s attempts to help children, and men’s own victimization in the light of CEDV. Most of the men reported that their children experienced different types of abuse, including neglect, witnessing the abuse of the father, physical and psychological abuse, and kidnapping. Effects of abuse on children varied from emotional suffering and estrangement to anger toward parents and turning against the father. Men’s attempt to intervene in the abuse of children included directly protecting children from abuse and staying in abusive relationships to protect the children. In addition, the men’s own victimization often took place in front of children. Finally, the men reported that their partners often used their children in their partners’ abuse of the men, such as through parental alienation. The implications of these findings for developing more gender-inclusive policies and programs for abused men and their children are discussed.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".