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Record W2591072610 · doi:10.7202/1038708ar

The Sexual Assault of Older Women: Criminal Justice Responses in Canada

2017· article· en· W2591072610 on OpenAlexafffundvenueabout
Isabel Grant, Janine Benedet

Bibliographic record

VenueMcGill Law Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of British Columbia
FundersLaw Foundation of British Columbia
KeywordsSexual assaultCriminologyCriminal justiceSexual violenceContext (archaeology)PsychologyEconomic JusticeSuicide preventionPoison controlPolitical scienceLawMedicineMedical emergencyHistory

Abstract

fetched live from OpenAlex

This article examines sexual violence against older women, a problem that has been largely hidden from view in the societal and legal discussion of sexual assault. The article identifies a significant disconnect between the social science description of sexual assault against older women, on the one hand, and the available case law, on the other. The social science literature suggests that older women are most likely to be sexually assaulted by somebody they know and that a disproportionate number of the sexual assaults against older women take place within care facilities. The case law, however, paints a very different picture of sexual violence against older women—a majority of the reported cases involve women attacked in their homes by strangers in the context of a robbery or home invasion. We argue that this portrayal of sexual violence against older women in the case law resembles that of the case law of sexual assault against younger women thirty years ago, before the women’s movement brought acquaintance and spousal sexual assault into the public eye. We conclude that these types of sexual assaults continue to be under-reported for older women, and we explore some of the reasons for the failure of the criminal justice system to respond to this group of complainants.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.770
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.324
Teacher spread0.284 · 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.

Study designNot applicable
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

Citations8
Published2017
Admission routes4
Has abstractyes

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