Seropolitics and the Criminal Accusation of HIV Non-Disclosure in Canada
Bibliographic record
Abstract
This paper examines the criminalization of HIV non-disclosure in Canada as a public health legal response. The analysis begins outside the public health framework to relate the criminalization of HIV to broader shifts in the relationship between life and law in contemporary forms of governance. It does this by drawing on the concepts of biopower and biopolitics to explain how the intersection of medical and legal knowledge creates an accusatorial framework that has made HIV criminalization possible. This idea is explored by tracing the historical development of the legal principle out of which the phenomenon has emerged (“fraud capable of vitiating consent to sexual relations”) and looking at how it has been applied in two contemporary HIV prosecution cases: R v. Aziga (2007) and R v. Ngeruka (2015). The second half of the paper examines the effectiveness of the criminal accusation of HIV non-disclosure as a public health legal response, focusing on its effect on advancing traditional public health goals. The discussion also points out how criminalization of HIV non-disclosure manifests broader tensions that have been recognized in public health legal responses to communicable disease, particularly the challenges of protecting the public while respecting individual rights. The paper concludes by arguing that control over blood blurs medical and legal forms of knowledge and power. This reflects a “seropolitical” landscape characterized by a criminal law accusatorial framework shaped by medical determinations of risk and harm.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.010 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".