Intimate partner violence and the male victim: an exploration of literature relating to the awareness of male victims of domestic violence and implications for social work practice
Bibliographic record
Abstract
This report seeks to explore the reaction to male victims of domestic violence within the UK. In literature and official guidance there is little mention of men. Looking at social work practice it is becoming increasing common to find that men have been subject to abuse from female partners, but the reaction from services and professionals appears to be lacking. Using research from journals and reports in the UK, USA, Canada, Australia and New Zealand, this paper seeks to review the main themes in the debate around male victims of domestic violence; social conditioning, gender stereotyping and feministic overtones, leading onto the reasons for a lack of response which include institutional genderism, bias and a lack of regard for changing societal gender roles, the issues that surround current services and provision, concluding with what potentially the UK could improve upon. Whilst in essence services, support, and funding for female protection will take precedent due to sheer numbers, this review gives an introduction to some of the topics within this field of discussion and pin points areas where improvements could be made that would considerably help and support male victims, whilst also improving professionals educational and working foundations.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".