Cripping and queering gender-based violence prevention: bridging disability justice, queer joy, and consent education
Bibliographic record
Abstract
Although frequently relegated to the periphery in conversations about gender-based violence prevention, the disabling impacts of traumatised subjectivity both affect survivors' abilities to fully participate in sex and contribute to survivors being more than twice as likely to be sexually (re)victimised compared to peers without trauma histories. In this paper, we seek to crip and queer approaches to gender-based violence prevention, particularly consent education, by learning from 2SLGBTQ+ and disabled trauma survivors' affective experiences of queer, crip sexual joy and the radically messy ways in which they establish their own care networks for deeply pleasurable sex through the principles of disability justice. Refusing pathologising understandings of survivors as those who need to be cured, we highlight traumatised subjectivity as emblematic of the ambiguity and ambivalence inherent in sex as well as the possibilities for caring, consensual sex that moves beyond the concept of consent employed in colonial, neoliberal capitalist societies' binary (Yes/No) consent laws. Drawing on the work of crip and queer theorists such as Mia Mingus, Alison Kafer, Leah Piepzna-Samarasinha, and J. Logan Smilges, we reveal how disability justice principles, such as interdependence, collective access, and access intimacy, offer transformative understandings for anti-violence efforts.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.050 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".