A mark that is no mark? Queer women and violence in HIV discourse
Bibliographic record
Abstract
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.
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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 | 0.005 | 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 | 0.011 | 0.057 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".