Beyond criminalization: reconsidering HIV criminalization in an era of reform
Bibliographic record
Abstract
PURPOSE OF REVIEW: This paper reviews recent studies examining the application of human immunodeficiency virus (HIV)-specific criminal laws in North America (particularly the United States and Canada). In the wake of the development of new biomedical prevention strategies, many states in the United States (US) have recently begun to reform or repeal their HIV-specific laws. These findings can help inform efforts to 'modernize' HIV laws (or, to revise in ways that reflect recent scientific advances in HIV treatment and prevention). RECENT FINDINGS: Recent studies suggest that HIV-specific laws disproportionately impact Black men, white women, and Black women. The media sensationally covers criminal trials under these laws, especially when they involve Black defendants who they often describe in racialized terms as predators. Activists contest these laws and raise concerns about new phylogenetic HIV surveillance techniques that have the potential to be harnessed for law enforcement purposes. SUMMARY: These findings collectively raise urgent concerns for the continued use of HIV-specific criminal laws. These policies disproportionately impact marginalized groups - particularly Black men. Media coverage of these cases often helps to spread misinformation and stigmatizing rhetoric about people living with HIV and promulgate racist stereotypes. Although well-intentioned, new phylogenetic HIV surveillance technologies have the potential to exacerbate these issues if law enforcement is able to gain access to these public health tools.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".