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Record W2207788488

Hearing the Sexual Assault Complaints of Women with Mental Disabilities: Evidentiary and Procedural Issues

2007· article· en· W2207788488 on OpenAlexaff
Janine Benedet

Bibliographic record

VenueeYLS (Yale Law School) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyContemplationWitnessCredibilityComplaintSexual assaultSocial psychologyCriminologyLawPoison controlSuicide preventionPolitical scienceMedicine
DOInot available

Abstract

fetched live from OpenAlex

When a woman with a mental disability makes a complaint of sexual assault, she must confront a criminal trial process that was not designed in contemplation of her as a witness. The requirements of repeated testimony under oath and the ability to be cross-examined are not always well-suited to the particular needs and capacities of women with mental disabilities. These problems are magnified by the tendency to infantilize women with mental disabilities, thereby diminishing their credibility and depicting them as hypersexual when they engage in any sexual activity. These stereotypes also manifest themselves in the application of evidentiary rules relating to evidence of sexual history and records in the hands of third parties. In this way, the disabilities of these women are not merely physiological in an "objective" sense, but are also constructed by the trial process itself. This article considers how the experiences of women with mental disabilities demand modifications to evidentiary and procedural rules in sexual assault cases in ways that are consistent with the right of the accused to a fair trial. It also uses these experiences to reflect on the purported tension between sexual freedom and protection from violence that is evident in the feminist literature on sexual assault. The authors argue that substantive equality demands greater efforts to ensure the full participation of women with mental disabilities in the criminal trial process.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.998

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 communication0.0000.000
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 head0.035
GPT teacher head0.329
Teacher spread0.294 · 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 designQualitative
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

Citations9
Published2007
Admission routes1
Has abstractyes

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