How Did They Do It? Analyzing the Landscape of HIV Criminalization Reform in the USA
Bibliographic record
Abstract
Abstract Introduction This study investigates the strategies associated with successful reform or repeal of HIV-specific criminal laws in the USA. These laws penalize people living with HIV (PLWH), often for behavior posing minimal or no risk of transmission, and perpetuate stigma and discrimination. Methods We applied McGarrell and Castellano’s integrative conflict model to analyze seven state-level legislative campaigns. Our dataset included legislative records and 135 media reports coded for thematic content related to stakeholder strategies and resonant framing. Results We identified five key strategies for successful reform: (1) making rhetorical appeals that resonate with issues of broad political import; (2) mobilizing “moral entrepreneurs” to champion legislative change; (3) building coalitions across advocacy groups; (4) leveraging broader legislative movements; and (5) spotlighting egregious legal outcomes to erode the legitimacy of existing laws. These strategies aligned legislative efforts with public concerns and political opportunities, leading to significant reform in diverse political contexts. Conclusions Reform campaigns are iterative and context-specific, requiring sustained advocacy and strategic alignment among stakeholders. Tailored approaches that align with state-specific political, social, and legal conditions enhance the likelihood of success. Policy Implications Policymakers and advocates should cultivate moral entrepreneurs to serve as public representatives, build cohesive coalitions with unified strategies, and deploy resonant frames that link HIV criminalization to issues of broad social import. Flexibility to seize emergent opportunities and focus on legislative strategies can help advance reform efforts. Long-term advocacy is critical to achieving meaningful change while avoiding unintended consequences, such as criminalizing other infectious diseases.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".