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Record W3124152775

A Situational Approach to Incapacity and Mental Disability in Sexual Assault Law

2013· article· en· W3124152775 on OpenAlexaboutno aff
Janine Benedet, Isabel Grant

Bibliographic record

VenueeYLS (Yale Law School) · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPlaintiffPsychologySituational ethicsContext (archaeology)LawCriminologySocial psychologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Prosecutions for sexual assault most often focus on whether the Crown has proven that the complainant did not consent to the sexual activity in issue, based on her subjective state of mind at the time of the offence. However, Canadian criminal law also provides that no consent is obtained where the complainant is incapable of consenting. In cases where the complainant has a mental disability affecting cognition or decisionmaking, prosecutors in Canada have been reluctant to argue that the complainant was incapable of consenting. In this article, the authors agree that claims of incapacity should be used sparingly, but contend that the doctrine of incapacity may be applicable and useful in some cases where the accused has exploited the complainant’s disability. They argue that capacity to consent to sexual activity should be defined situationally, rather than as an all-or-nothing measure. Since consent is given to a specific person in a specific circumstance, incapacity should be also assessed by reference to the particular context of the case. This approach to incapacity has been adopted in English and American cases, which provide examples of how it might be applied and understood in Canada. A situational definition of incapacity offers some legal recognition of the particular challenges faced by women with mental disabilities with respect to sexual abuse, without disqualifying them from any lawful sexual activity in other contexts.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.291
Teacher spread0.262 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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