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Record W3037992251 · doi:10.1080/0966369x.2020.1781795

‘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

2020· article· en· W3037992251 on OpenAlexaff
Jennifer M. Kilty

Bibliographic record

VenueGender Place & Culture · 2020
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPrisonStigma (botany)TransgenderMental healthHuman immunodeficiency virus (HIV)IntersectionalityCriminologyPsychologyDistressGender studiesIdentity (music)Social psychologyPsychiatryMedicineSociologyClinical psychologyFamily medicine

Abstract

fetched live from OpenAlex

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.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.060
GPT teacher head0.338
Teacher spread0.279 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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