Bibliographic record
Abstract
Recent efforts to make sense of the Violent Woman have revolved around three different (yet not disconnected) constructs: “victim,” “mad,” and “bad.” One issue that has been missing in discussions of the Violent Woman, however, is how women who use violence constitute themselves. In this study, using in-depth interviews with 18 criminalized women, we aim to uncover the women's own discursive constructions and the discourses they draw from in their efforts at framing identity. How and where is violence situated in the women's accounts of their lives? How do the women constitute themselves? What discourses do they draw from (and resist)? Do the prevailing constructs of the Violent Woman as “victim,” “mad,” or “bad” have resonance for these women? In the process, we argue that each of these constructs, while having some resonance in the women's accounts, fails to capture the complexity of their lives. Because identity is fractured and multiple, violence in the lives of criminalized women (their own violence and that which is directed at them) cannot be rendered plausible by imposing a master status template. We conclude by considering the implications of this analysis for making sense of women's 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".