Theory and HIV/AIDS: Investigating Disability Theory as a New Conceptual Framework for HIV and AIDS-Related Stigma
Bibliographic record
Abstract
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.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.074 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".