Who? What? Where? When? And with What Consequences? An Analysis of Criminal Cases of HIV Non-disclosure in Canada
Bibliographic record
Abstract
Abstract The use of criminal-law powers to respond to people with HIV who place others at risk of HIV infection has emerged as a focal point of AIDS advocacy at global, national, and local levels. In the Canadian context, reform efforts that address the criminalization of HIV non-disclosure have been hampered by the absence of data on the contours, scale, and outcomes of criminalization. This article responds to that gap in knowledge with the first comprehensive analysis of the temporal trends, demographic patterns, and aggregate outcomes of Canadian criminal cases of HIV non-disclosure. The authors draw on insights into the role that rendering social phenomena in numerical terms plays for the governance of social life in order to make criminalization “visible” in ways that might contribute to activist responses. The article examines temporal trends, demographic patterns, and outcomes separately. In each instance, the pattern or trend identified is described, potential explanations for findings are offered, and an account is given of how the data have informed efforts to reform criminal law. Particular attention is paid to the following key findings: a sharp increase in criminal cases that began in 2004; the large proportion of recent criminal cases involving defendants who are heterosexual Black, African, and Caribbean men; and the high proportion of criminal cases resulting in conviction. The article closes with suggestions for future research.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".