‘I just wanted them to <i>see</i> me’: Intersectional stigma and the health consequences of segregating Black, HIV+ transwomen in prison in the US state of Georgia
Bibliographic record
Abstract
This paper mobilizes the findings generated from in depth, in person interviews conducted with ten Black, HIV+ transgender women who had previously served time in local jails and prisons in the Atlanta region of the state of Georgia, USA. The paper explores how intersectional stigma emerges in the carceral environment in relation to the women’s multiple identity locations and the ways that HIV and transgender stigma in particular are linked to two harmful correctional practices. First, participants revealed that the ‘layering’ of these different forms of stigma resulted in institutional coercion to suppress their gender identity and to the inappropriate use of solitary confinement, both of which led to increased mental and emotional distress during their period of incarceration as well as after they were released from jail or prison. Second, participants were also frequently denied access to or had irregular access to their HIV medication and hormone replacement therapies (HRT), which can have significant mental and physical side effects. In carceral environments, the manifestation of intersectional stigma influences how individuals are treated by staff, where they were housed and how their institutional time is managed; in other words, it contributes to mapping their carceral experience.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".