AIDS Exceptionalism: On the Social Psychology of HIV Prevention Research
Bibliographic record
Abstract
The current analysis considers the human immunodeficiency virus (HIV) prevention research record in the social sciences. We do so with special reference to what has been termed “AIDS Exceptionalism”—departures from standard public health practice and prevention research priorities in favor of alternative approaches to prevention that, it has been argued, emphasize individual rights at the expense of public health protection. In considering this issue, we review the historical context of the HIV epidemic; empirically demonstrate a pattern of prevention research characterized by systematic neglect of prevention interventions for HIV‐infected persons; and articulate a rationale for “Prevention for Positives,” supportive prevention efforts tailored to the needs of HIV+ individuals. We then propose a social psychological conceptualization of processes that appear to have influenced developments in HIV prevention research and directed its focus to particular target populations. Our concluding section considers whether there are social and research policy lessons to be learned from the record of HIV prevention research that might improve our ability to address effectively, equitably, and in timely fashion future epidemics that play out, as HIV does, at the junction of biology and behavior.
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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 | 0.039 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.007 | 0.073 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".