The Politics of Triage: International Aid and AIDS Care in Northern Uganda
Bibliographic record
Abstract
Uganda has been considered an AIDS success story since the late 1990s when prevalence rates decreased around the country.Recognizing Uganda's unique AIDS response, this thesis seeks to analyze HIV/AIDS in Uganda and challenges normative understandings of Uganda's 'success', specifically, in Northern Uganda.It explores how Ugandan HIV/AIDS policies targeting NGOs and the 'community' have depoliticized HIV/AIDS, creating new inequalities through triaged care.To understand the production of inequality, this thesis explores how clients and aid workers define 'vulnerability' and how relationships affect aid allocation.Newly emerging arenas of stigma are examined in order to challenge normative attitudes of HIV-status disclosure in health campaigns by demonstrating how HIV-positive people are facing new stigmas when they are incapable of being 'productive citizens.'As humanitarian aid leaves Uganda, HIV/AIDS NGOs seek to relieve the issue of aid dependency through development initiatives.This thesis ends by challenging development's understanding of 'us' versus 'them'.vi Map of Uganda This is a map of Uganda's 88 districts as of July 2006.The number of Uganda's districts increased to 111 as of August 2 nd 2010.United Nations Office for the Coordination of Humanitarian Affairs (OCHA).2006 Map of Uganda Including New Districts by Region.http://reliefweb.int/sites/reliefweb.int/files/resources/ACFA0 64C7C4EA2138525734400468CE5-ocha_REF_uga061031.pdf, accessed
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".