Considering Sex/Gender-Based Violence as a Form of Hate: The Invisibility of Sex and Gender
Bibliographic record
Abstract
Globally, there is no shortage of examples demonstrating lethal and non-lethal violence motivated, at least in part, by a hatred of women and girls because of their sex or gender. Such violence is not a new phenomenon. Despite this, there remains little consideration of sex/gender-based violence (S/GBV) motivated by hatred in the hate/bias crime literature, including a recent comprehensive review published in this journal. Drawing from a comprehensive scoping review of international literature, this article discusses why this might be the case, identifying both the benefits and challenges of treating sex/gender-motivated violence as a form of hate. The review examined primarily legal- and case-based analyses, grey literature, and some empirically based research articles, both qualitative and quantitative, the latter of which largely had only a peripheral focus on the question posed-the consideration or recognition of sex/gender-motivated hate that leads to violence. Themes surrounding benefits and challenges of doing so were identified. Among the findings was that, while there are valid arguments for and against the inclusion of, or emphasis on, S/GBV as a form of hate, what is largely absent from the body of literature is systematic, empirically based evidence examining the validity of the arguments identified, particularly in recent years. The article concludes by highlighting four broad research and policy priorities which can further (or arguably begin) the conversation about the role of hate in S/GBV.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".