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Record W3201848122 · doi:10.1558/genl.20884

Intersections of race and gender in sexual assault trials

2021· article· en· W3201848122 on OpenAlexaff
Susan Ehrlich

Bibliographic record

VenueGender and Language · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsYork University
Fundersnot available
KeywordsIntersectionalityRacismGender studiesSociologyCriminologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

This essay traces the development of intersectionality theory within the field of language and gender in relation to research on the language of rape trials. In early work on the topic, I used Judith Butler’s notion of the ‘rigid regulatory frame’ to understand the cultural intelligibility of certain kinds of rape victims in the legal system and the unintelligibility of others. But the inequities that complainants often experience in rape trials are not merely the result of sexism; rather, it is sexism and racism which together interact to disadvantage complainants and protect white male perpetrators, who occupy a privileged position within these contexts vis-à-vis men of colour. In line with recent work in the field, I end with an analysis of a rape case that demonstrates the necessity of attending to nonhegemonic masculinities and intersectionality.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0160.051
Scholarly communication0.0140.012
Open science0.0020.013
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.070
GPT teacher head0.370
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2021
Admission routes1
Has abstractyes

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