As Wives Too Beat Husbands: Another Look at the Socio-Legal Narratives of Domestic Violence
Bibliographic record
Abstract
In the conventional human rights, socio-legal and related discourse of domestic violence, focus has preponderantly been on men as the perpetrators, with wives or women as victims. This may be due to some factors. One is the entrenched empathy and solidarity with women as the “weaker sex” and usual victims. In such context, instances of women’s acts of domestic violence tend to be perceived readily as “exceptional cases” where the women perpetrators “must have been pushed to the wall”. Another possible factor is the mind-set to readily assume that wife-on-husband violence is “unlikely” or “improbable”, based on gender differences in size and strength, coupled with the man being the domineering personality and “head” in conventional spousal heterosexual relationships. A third factor may be that women can readily and comfortably speak out about their experiences as victims of domestic violence with expectations of societal interventions. Conversely, due to ego or masculine dignity male victims of domestic violence may keep silent, thus leaving, mainly, the voices of women resonating as victims in the pertinent narratives. However, studies, on a notable scale, are showing the reality of males being victims of domestic violence perpetrated by females. This paper seeks to address the foregoing and related issues in the context of human rights and similar aspects. Among the issues to be engaged will the factors contributory to the muffling of the voices of male victims and need for a pragmatic balance in the protection of women and men against domestic violence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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".