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Record W1530395027

The Boundaries of the Criminal Law: The Criminalization of the Non-Disclosure of HIV

2008· article· en· W1530395027 on OpenAlexaffabout
Isabel Grant

Bibliographic record

VenueeYLS (Yale Law School) · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCriminalizationImprisonmentCriminal lawLife imprisonmentCriminologyLawSupreme courtPunishment (psychology)PrisonPolitical sciencePublic healthContradictionSentencePsychologySocial psychologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

In this paper, the author examines the trend toward the increased criminalization and punishment of persons with HIV who fail to inform their sexual partners of their HIV-positive status. Since the Supreme Court of Canada's decision in R. v. Cuerrier, such behaviour may constitute aggravated assault or aggravated sexual assault, the latter offence carrying a maximum sentence of life imprisonment. The paper surveys the Canadian case law and highlights the trend towards the imposition of increasingly harsh sentences. After reviewing public-health and criminal law options for dealing with non-disclosure of one's HIV status, the author concludes that criminal law should only be invoked in the most serious circumstances and only where all other public health measures have been exhausted. Criminal law should be reserved for individuals who demonstrate a pattern of non-disclosure either over time or with different sexual partners. The author also explores the social and legal reasons behind the apparent contradiction that, despite the improved prognosis for persons with HIV sentences for those who knowingly transmit the virus have become increasingly severe.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.184
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.030
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations7
Published2008
Admission routes2
Has abstractyes

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Same venueeYLS (Yale Law School)Same topicCriminal Justice and Corrections AnalysisFrench-language works237,207