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Record W7005801404

A scan of Canadian reported cases of the criminalization of HIV/AIDS non-disclosure: 1989 to 2024.

2025· article· en· W7005801404 on OpenAlexaboutno aff

Bibliographic record

VenueJournal System (Mount Royal University) · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCriminalizationPlaintiffGovernment (linguistics)Criminal justiceCriminal lawPublic healthRedress
DOInot available

Abstract

fetched live from OpenAlex

We explored the criminalization of HIV non-disclosure in Canada to determine how the criminal justice system responded to non-disclosure in light of medical advancements related to transmission. The study included a review of literature, government reports, laws, and policies to reveal numerous prosecutions between 1989 to 2024. We performed a review of 162 reported cases to assess the types of offences and outcomes, prosecution distribution across the country, and accused and complainant characteristics. The literature points out that Canadian courts often overlook medical advancements, disproportionately criminalizing HIV non-disclosure compared to other sexually transmitted infections. Our findings confirmed that, and revealed that over time, public nuisance, sexual, and criminal negligence offences were applied. Accused were predominantly male, Caucasian, and most cases were prosecuted in Ontario and Québec, and accused and complainants were most often known to each other as casual, dating, or in long-term relationships. We argue for the reassessment of the criminalization of HIV non-disclosure, considering Directive 5.12 and Standing Committee’s Report (2019) both of which are a move in the right direction; however, charges continue post 2018, as do the stigmatizing effects of prosecution, and the need for more education and public health interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.264
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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