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Record W578120660 · doi:10.59962/9780774856003

Judicial Decision Making in Child Sexual Abuse Cases

2008· book· en· W578120660 on OpenAlexaboutno aff
Margaret M. Wright

Bibliographic record

VenueUniversity of British Columbia Press eBooks · 2008
Typebook
Languageen
FieldSocial Sciences
TopicLaw, Rights, and Freedoms
Canadian institutionsnot available
Fundersnot available
KeywordsChild sexual abuseIndex (typography)CriminologySexual abusePsychologyChild abuseLawPolitical scienceHuman factors and ergonomicsMedicineComputer sciencePoison controlMedical emergencyWorld Wide Web

Abstract

fetched live from OpenAlex

In the 1980s, Canada witnessed a public outcry over child sexual abuse cases that were being reported in the media. Elected officials sought a remedy not through policy changes or other social mechanisms but rather through legal reforms. Amendments were made to the Criminal Code of Canada and sexual assault was redefined. The word “rape” was replaced with a continuum of sexual assault categories intended to reflect the full range of sexually intrusive behaviours. Most women’s groups, having fought for recognition of harm done to women and children, supported this legislation, though some questioned the approach Margaret Wright examines how the courts have dealt with child sexual abuse cases since then and what effect the “resort to law” has had. Analyzing the sentencing phase of these cases, she demonstrates that although the laws may have changed, their interpretation still depends on the social construction of children at the court level and on judges’ own understanding of what constitutes child sexual abuse. Judicial Decision Making in Child Sexual Abuse Cases is a rich and detailed study of the court process that will be welcomed by students and scholars of law and society, social work, criminal justice, and social policy

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)0.011
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.033
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.027
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0070.010
Scholarly communication0.0090.004
Open science0.0030.004
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2008
Admission routes1
Has abstractyes

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