Equal Victims or the Usual Suspects? Making Sense of Domestic Abuse Against Men
Bibliographic record
Abstract
This article reports on research commissioned to address the topic of domestic abuse against men in Scotland. The research addressed three key questions: (1) Why do male victims appear much more frequently in crime survey data than in recorded crime statistics? (2) Are there significant differences in the nature and frequency of domestic abuse experienced by men and women? (3) In what kinds of relationships does domestic abuse against men occur? The article explains that the relative absence of male victims in the domestic abuse statistics gathered by the Scottish police can be accounted for in terms of gender differences in experiences of victimisation and reporting patterns. Drawing upon in-depth interview material elicited from a sample of men originally counted as ‘male victims’ in the Scottish Crime Survey, the article also argues that statistics collated on the basis of crime survey data overstate men's experiences of domestic abuse. The article concludes with a discussion of the methodological and policy implications that should be drawn from this finding.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".