Bibliographic record
Abstract
The HIV epidemic in southern Africa has important consequences for economic development.The epidemic could be stopped by a universal test and treat policy, as antiretroviral drugs block the spread of the virus.However, demand for HIV testing and treatment are surprisingly low.This paper develops a model in which the decision to seek an HIV test is a signal of infection, and those who seek a test are subject to statistical discrimination from potential sexual partners.We evaluate an information experiment designed to test the theory, and find evidence that this form of discrimination is a significant barrier to HIV testing.In particular, we provide information at the community level on the public benefit of antiretroviral therapy: because the drugs prevent HIV transmission, a person who is tested and treated for HIV is a relatively safe sexual partner.This information reduces discrimination and increases HIV testing, with the strongest effects in communities where the new information becomes common knowledge.The results demonstrate that discrimination towards HIV positive individuals can be due to rational behavior by a misinformed public, and that providing new information can be an effective way to mitigate its effects.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".