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Record W3171430140 · doi:10.1111/1467-9566.13290

When biographical disruption meets HIV exceptionalism: Reshaping illness identities in the shadow of criminalization

2021· article· en· W3171430140 on OpenAlexafffundabout
Michael Orsini, Jennifer M. Kilty

Bibliographic record

VenueSociology of Health & Illness · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsWomen's and Gender Studies et Recherches FéministesUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsCriminalizationSociologyMedicalizationCriminologyExceptionalismShadow (psychology)LawPsychologyPolitical sciencePsychiatryPsychoanalysisPolitics

Abstract

fetched live from OpenAlex

Drawing on interviews with civil society actors in the AIDS Service Organization (ASO) sector in Canada, this article explores how these actors contribute to shaping the illness identities of people living with HIV/AIDS in the shadow of efforts to criminalize exposure to HIV. While the biographically disruptive qualities associated with an HIV diagnosis have been addressed in the medical sociology literature, we turn our attention to the key role played by ASOs as interlocutors in this process. Paying specific attention to the intersection of processes of medicalization and criminalization, we ask how they are re-stigmatizing a condition that has shifted in the public consciousness from its earlier association with deviance and moral culpability. One important implication of our findings concerns the need to take greater account of how the illness identity and experience can be shaped by a 'biography of telling', of a renewed pressure to disclose intimate details of one's health status as a way to perform responsible practices of citizenship.

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)0.006
metaresearch head score (Gemma)0.016
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0300.060
Scholarly communication0.0130.010
Open science0.0020.017
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.367
Teacher spread0.323 · 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

Citations8
Published2021
Admission routes3
Has abstractyes

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