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Record W4205413803 · doi:10.1097/coh.0000000000000715

Beyond criminalization: reconsidering HIV criminalization in an era of reform

2022· article· en· W4205413803 on OpenAlexaboutno aff

Bibliographic record

VenueCurrent Opinion in HIV and AIDS · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSex work and related issues
Canadian institutionsnot available
Fundersnot available
KeywordsCriminalizationMisinformationHuman immunodeficiency virus (HIV)Law enforcementRhetoricPublic healthEnforcementUnintended consequencesDecriminalization

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.087
GPT teacher head0.364
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2022
Admission routes1
Has abstractyes

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