Solutions to Ending the Global Social Problem of HIV Criminalization
Bibliographic record
Abstract
Edwin Bernard, HIV Justice Network Eric Mykhalovskiy, York University The Problem HIV criminalization involves using the criminal law to punish people living with HIV (PLHIV) for engaging in certain activities, most commonly not disclosing one’s HIV-positive status to a sex partner, potentially exposing another person to HIV, or allegedly transmitting the virus. It can also involve sentence enhancements for PLHIV who are found guilty of other “crimes,” notably sex work or simple assaults such as spitting at, or biting, another person. HIV criminalization is a complex issue that encompasses the process of defining activities as crimes, enforcing laws, and prosecuting particular cases. It raises important concerns about how societies respond to HIV prevention and about the harmful consequences of using the criminal justice system to address public health problems. Examples of HIV criminalization as a global phenomenon include: • Zimbabwe: A young woman on HIV medications was convicted under an overly broad, vague HIV-specific law that criminalizes “deliberately infecting another person” and received a suspended sentence of five years in prison. Her partner tested HIV-negative. • Singapore: A young man received a year in prison for allegedly exposing another man to HIV while performing oral sex, an act that posed no HIV transmission risk. • U.S.: A young man on HIV medications, virally suppressed, and therefore unable to transmit HIV sexually, was sentenced to 25 years in prison for a one-time sexual encounter that involved performing oral sex (without a condom) and anal sex with a condom. He was also required to register as a lifetime sex offender. HIV criminalization dates back to the early years of the pandemic (late 1980s/early 1990s). Laws that specifically criminalize HIV were first enacted at this time and criminal cases related to HIV non-disclosure, exposure and transmission began to be prosecuted first in the U.S. and then Canada. HIV criminalization was partly justified by some policy makers, media, and legal scholars, claiming that it would help reduce HIV transmission. That claim has never been supported by research evidence. Despite huge advances in the science of HIV transmission risks and harms and in research showing that HIV criminalization has many unintended negative impacts on public health, HIV criminalization continues unabated in many places in the world, leading to human rights violations.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".