The Power to Name: Conceptualizing Domestic Violence as Violence Against Women
Bibliographic record
Abstract
Since the early 1970s, feminist researchers and advocates have identified violence against wives or female partners as a serious and pervasive social issue, resulting in changes to housing, social services, and legal reforms. Recently, some family violence researchers, sociologists, and men’s activists have challenged feminist claims that women are the primary victims of intimate partner violence; citing numerous studies that suggest men are frequently victims of violence by their female intimate partners and arguing that, because of symmetrical prevalence rates found in numerous studies, violence occurring within intimate relationships represents “mutual combat” and should be conceptualized as gender-neutral. Feminist researchers and women activists oppose gender-neutral conceptualizations and argue that violence is indeed gendered; and issues of context, meaning, and consequences should be examined before making claims of gender symmetry. They contend that the issue should be gender specific and should be viewed as “violence against women”, instead of more gender-neutral conceptualizations as “domestic violence” or “spousal abuse’. Not surprisingly, a heated debated has erupted among researchers, policymakers, and community activists about the gendered nature of intimate partner violence. Specifically, the debate centers on the rate of women’s use of violence against their intimate partners and the degree of harm inflicted by women. This debate about the gendered nature of intimate partner violence will be examined. I conclude by suggesting that a feminist and gender-specific theoretical framework is most useful in understanding heterosexual intimate partner 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.067 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| 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".