Bibliographic record
Abstract
Abstract This chapter will seek to add insight on the lived experiences of women subjected to non-fatal, non-consensual violence in sex (NCVS) by men. The chapter will draw on primary research conducted by the author in the Spring and Summer of 2020, comprising in-depth interviews with eight women and a survey of 84 women, all of whom had experienced NCVS, often from multiple perpetrators. It will summarize the acts to which women were subjected (often life-threatening in nature), the long-term impacts on women, and the ways in which men minimized and re-packaged their violence. It will make the case that NCVS – often dismissed as ‘rough sex gone wrong’ – is a particularly insidious form of violence against women and girls. The chapter will highlight how women’s sense-making processes around NCVS are often hampered by legal definitions of sexual violence, which left women wondering ‘what category to put it in’. Using Fricker’s (2007) concept on ‘epistemic injustice’, it will emphasize the need for a ‘shared tools of social interpretation’ (p. 6) around NCVS, alongside any legal changes, and the importance of campaigns like We Can’t Consent To This in giving language to women’s often unspoken experiences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".