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Record W4401483651 · doi:10.1080/13691058.2024.2380768

Cripping and queering gender-based violence prevention: bridging disability justice, queer joy, and consent education

2024· article· en· W4401483651 on OpenAlexafffund
Jessica Wright, Caitlin A. Manuel

Bibliographic record

VenueCulture Health & Sexuality · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicFeminism, Gender, and Sexuality Studies
Canadian institutionsCarleton UniversityMacEwan University
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsQueerAmbivalenceSubjectivityGender studiesSexual violenceSociologyPsychologyCriminologySocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.133
GPT teacher head0.457
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2024
Admission routes2
Has abstractyes

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