Critical bioethics in the time of epidemic: The case of the criminalization of HIV/AIDS nondisclosure in Canada
Bibliographic record
Abstract
This article highlights the ethically uncertain and emotionally charged climate that governs the criminalization \nof HIV nondisclosure in Canada. Focusing on AIDS Service Organizations (ASO), we suggest that interlocutors \nperform critical work that helps people living with HIV/AIDS make sense of their rights and responsibilities. \nSemi-structured interviews with 62 ASO staff across Canada revealed this shifting landscape of HIV advocacy \nin the age of criminalizing HIV nondisclosure. Drawing on a critical bioethics approach that is informed by \nconsidering the role of emotion in decision-making, this article critiques the liberal model of the rational actor \nthat is central to traditional discussions of bioethics and law. Our findings suggest that ASO workers have \nvarying degrees of knowledge about the intricacies of legal duties of disclosure, which affect how they balance \ntheir own emotions and thoughts about nondisclosure with their professional duties to provide support and \ncounselling. Ultimately, we argue that critical bioethics in the context of criminalization commands us to \nappreciate the inherently affective nature of the environment in which bioethical decisions are made.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.100 | 0.046 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.023 |
| 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".