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Record W2054692282 · doi:10.1080/13691058.2012.738430

A mark that is no mark? Queer women and violence in HIV discourse

2012· article· en· W2054692282 on OpenAlexafffund
Carmen H. Logie, Margaret F. Gibson

Bibliographic record

VenueCulture Health & Sexuality · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsUniversity of CalgaryUniversity of TorontoWomen's College Hospital
FundersCanadian Institutes of Health ResearchSvenska Forskningsrådet Formas
KeywordsQueerLesbianGender studiesQueer theorySociologyCriminologyHeterosexualityHomosexualityPsychology

Abstract

fetched live from OpenAlex

Lesbian, bisexual and queer women are invisible and ignored in HIV discourse, as epidemiological classifications result in their institutionalised exclusion from risk categories. Simultaneously, these women live with HIV, often in situations of societal exclusion and under threat of violence. In this paper, we consider the connections between discourse and violence to examine how both are reproduced through, applied to and dependent upon people. The ways lesbian, bisexual and queer women do (or do not) appear in HIV discourse tells us much about how people and categories operate in the global pandemic. The fault-lines of lesbian, bisexual and queer women's constrained visibility in HIV discourse can be seen in situations where they are exposed to HIV transmission through homophobic sexual assault. In dominant HIV discursive practices, such homophobic assault leaves Judith Butler's 'mark that is no mark', recording neither its violence nor its 'non-heterosexuality'. Structural violence theory offers a means to understand direct and indirect violence as it pertains to HIV and lesbian, bisexual and queer women. We call for forms of modified structural violence theory that better attend to the ways in which discourse connects with material realities. Our theoretical and epidemiological lens must be broadened to examine how anti-lesbian, bisexual and queer-women bias affects transnational understandings of human worth.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.632

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 studies0.0000.000
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 head0.031
GPT teacher head0.379
Teacher spread0.348 · 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.

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

Citations50
Published2012
Admission routes2
Has abstractyes

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