HIV responsibilisation: Stigma, disclosure, and care in the age of 90‐90‐90
Bibliographic record
Abstract
Abstract The announcement that World AIDS Day would mark its 30th anniversary with the theme “know your status” was the result of significant advancements in the global response to the HIV/AIDS epidemic. In making this declaration, UNAIDS emphasised that knowing one's status is crucial to achieving the 90‐90‐90 targets, namely that by 2020, 90% of all people living with HIV will know their status, receive sustained antiretroviral therapy, and have viral suppression. Far removed from an earlier period when access to antiretroviral therapy was limited or unavailable, the “know your status” campaign represents a more hopeful moment. Yet even with its laudatory goal, the campaign reflects a larger trend in global health emphasising that being responsible for one's own health obligates caring for the health of others. The intention of this paper is to engage with geographic scholarship on care to examine how living with HIV involves both personal responsibility and responsibilisation. Drawing from fieldwork in rural South Africa, the paper outlines the challenges for those living with HIV, particularly when stigma and other needs remain stark. We conclude by identifying points of convergence and divergence between theories of responsibility, responsibilisation, and an ethics of care.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".