Ten reasons to oppose the criminalization of HIV exposure or transmission
Bibliographic record
Abstract
Recent years have seen a push to apply criminal law to HIV exposure and transmission, often driven by the wish to respond to concerns about the ongoing rapid spread of HIV in many countries. Particularly in Africa, some groups have begun to advocate for criminalization in response to the serious phenomenon of women being infected with HIV through sexual violence or by partners who do not reveal their HIV diagnoses to them. While these issues must be urgently addressed, a closer analysis of the complex issues raised by criminalization of HIV exposure or transmission reveals that criminalization is unlikely to prevent new infections or reduce women's vulnerability to HIV. In fact, it may harm women rather than assist them, and have a negative impact on public health and human rights. This paper is a slightly revised version of a document originally released in December 2008 by a coalition of HIV, women's and human rights organizations. It provides ten reasons why criminalizing HIV exposure or transmission is generally an unjust and ineffective public policy. The obvious exception involves cases where individuals purposely or maliciously transmit HIV with the intent to harm others. In these rare cases, existing criminal laws - rather than new, HIV-specific laws - can and should be used.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".