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Record W3157567761 · doi:10.24908/iqurcp.8703

Theory and HIV/AIDS: Investigating Disability Theory as a New Conceptual Framework for HIV and AIDS-Related Stigma

2016· article· en· W3157567761 on OpenAlexvenueno aff
Tim Wyman-McCarthy

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsStigma (botany)Human immunodeficiency virus (HIV)PsychologyConceptual frameworkQueer theoryDisability studiesSociologySocial psychologyMedicineGender studiesPsychiatryHuman sexualitySocial scienceFamily medicine

Abstract

fetched live from OpenAlex

The title of Paula Treichler’s 1999 book, How to Have Theory in an Epidemic, raises an important question: is it possible, moral, or even useful to approach theoretically an event as real and as devastating as the HIV and AIDS epidemic in South Africa? This presentation will investigate the way in which existing attempts to theorize HIV and AIDS-related stigma have been seen as inadequate, and how the newly emerging strand of critical theory known as ‘disability studies’ may provide a particularly useful vocabulary by which to discuss the stigmatization of those living with HIV or AIDS. This line of inquiry leads to a number of further questions: What is the relationship between disease and disability? Is it fair to approach an issue as complicated as HIV and AIDS-related stigma through one theoretical lens, or is an interdisciplinary approach linking Health Studies, Queer Theory, Postcolonial Theory, and Disability Theory necessary? And finally, what are the useful implications of linking Disability Theory with the study of HIV and AIDS-related stigma? The presentation will approach these questions by placing into relationship critical work from anthropology, disability studies, health studies as well as creative work in the form of Siphiwo Mahala’s 2007 novel When a Man Cries.

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)0.010
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.020
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.107
GPT teacher head0.409
Teacher spread0.302 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2016
Admission routes1
Has abstractyes

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