Rethinking Risk: The Relevance of Condoms and Viral Load in HIV Nondisclosure Prosecutions
Bibliographic record
Abstract
An HIV-positive individual who fails to disclose his or her status to a sexual partner may face charges ranging from nuisance to murder for such behaviour, with the most common charges being aggravated assault and aggravated sexual assault. The number of prosecutions in Canada against individuals who fail to disclose their HIV-positive status to their sexual partners has risen over the last ten years. At the same time, scientific advancements in treatment options and our understanding of transmission, condom usage, and viral load are constantly influencing the assessment of the risk that nondisclosure poses to the complainant in any given case. The author reviews the recent case of R. v. Mabior, the first judgment in Canada to criminalize nondisclosure in the context of protected sex. She argues that encouraging condom use is so important, and that the use of condoms reduces the risk of transmission so significantly, that the criminal law should distinguish between protected and unprotected sex in cases of nondisclosure. The author proceeds to critique the trial judge's reliance on viral load as a factor in determining whether nondisclosure poses a significant risk of serious bodily harm under the test established in Cuerrier. The author argues that the accused's viral load, unlike condom use, is not a manageable standard on which to base culpability.
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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 | 0.016 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".