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Record W2738408778 · doi:10.1097/qad.0000000000001570

The harms of HIV criminalization

2017· letter· en· W2738408778 on OpenAlexaff
Alexander McClelland, Martin French, Eric Mykhalovskiy, Marilou Gagnon, Elizabeth Manning, Ryan Peck, Chad Clarke, Tim McCaskell

Bibliographic record

VenueAIDS · 2017
Typeletter
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsHIV Legal NetworkUniversity of OttawaYork UniversitySimon Fraser UniversityConcordia University
Fundersnot available
KeywordsCriminalizationHuman immunodeficiency virus (HIV)CriminologyCriminal lawHIV diagnosisState (computer science)LawMedicinePolitical scienceSociologyPsychologyFamily medicineAntiretroviral therapy

Abstract

fetched live from OpenAlex

As academics, advocates, including people living with HIV, we are writing to welcome the findings, recently published in this journal, suggesting the ineffectiveness of invoking the criminal law as a tool of HIV prevention [1]. Using the American Centers for Disease Control and Prevention data, Sweeney et al.[1] searched for correlations between rates of diagnosis of HIV (2001–2010) and AIDS (1994–2010) and the presence of state laws that criminalize so-called ‘HIV exposure’. In 30 states that had such laws, Sweeney et al.[1] found ‘no association between HIV or AIDS diagnosis rates and criminal exposure laws across states over time’. We strongly encourage developing new knowledge about the implications of criminal laws for HIV prevention. We also recognize that Sweeney et al.[1] published a ‘concise communication’ format article, which undoubtedly limited what could be said. Despite this, we feel it imperative to highlight some omissions and assumptions in the work that precluded this article from making a stronger statement about the harms of HIV criminalization. First, this research does not sufficiently address the myriad negative impacts that HIV laws have had on the lives of people living with HIV. Sweeney et al.[1] seem unaware of the growing body of social science work that has theorized and documented, in empirical terms, these harmful effects [2]. If this evidence of harmful effects had been considered, we think Sweeney et al.[1] might have actually come to a less qualified conclusion. They are careful to note the limitations of their ecological analysis, but they could go further to underscore that, although they detected no association, there is research that has documented how such laws interfere with the work of HIV prevention [3]. Second, the quantitative approach employed relies on a form of logic that reinforces assumptions about the criminal law's rationality and neutrality in a way that is potentially troubling, as it fails to recognize two important factors that have been explored by social science researchers: the ways in which criminal laws and courts can be highly irrational and contingent; [4] and historically how criminal laws have been organized around the regulation, control, and incapacitation of populations (e.g., people of color, people with disabilities, people who live in poverty, gay, lesbian, and trans people, and people who live with forms of communicable disease, among others) [5]. Research organized with the underpinning assumptions that HIV criminal laws are rationally intended to prevent transmission of the virus, and that legislators will simply uptake scientific or public health knowledge to promote the goals of science and public health may be unintentionally misguided. Third, Sweeney et al. [1] noted that ‘state governments have been encouraged to review criminal laws to ensure they reflect current science on transmission risk as well as further public interest and public health’ (p. 11). To strengthen this observation, they might have also acknowledged law reform efforts, now underway in many jurisdictions, led by those living with, and most affected by, HIV (see, among others, HIV Justice Network, and the Global Commission on HIV and the Law). A lack of engagement with these efforts seems disconnected from the growing expectation that social science research be made meaningful in the real world. Although the study by Sweeny et al.[1] may have productive possibilities for advocacy, it was unfortunately too silent on the harms of exposure laws. Acknowledgements Conflicts of interest There are no conflicts of interest.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.407
Threshold uncertainty score0.448

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 integrity0.0000.001
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 head0.071
GPT teacher head0.359
Teacher spread0.288 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations9
Published2017
Admission routes1
Has abstractyes

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