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Record W2337303333

Hearing the Voices of Children in Canada’s Criminal Justice System: Recognising Capacity and Facilitating Testimony

2011· article· en· W2337303333 on OpenAlexaffabout
Nicholas Bala, Angela D. Evans, Emily Bala

Bibliographic record

VenueSSRN Electronic Journal · 2011
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of TorontoQueen's University
Fundersnot available
KeywordsStatutory lawLegislationLegislatureEconomic JusticeCriminal justicePolitical scienceLawQuarter (Canadian coin)Administration of justiceCriminal lawAdministration (probate law)CriminologyPsychologyHistory
DOInot available

Abstract

fetched live from OpenAlex

This paper reviews common law and statutory developments in the treatment of children as witnesses in Canada’s criminal justice system, where children who are victims of abuse testify with increasing frequency. Historically, children were regarded as inherently unreliable witnesses, and there were no provisions to accommodate their needs and vulnerabilities; this treatment by the justice system contributed to the abuse and exploitation of children. Reflecting a growing body of research on child development, and a better understanding of the effects of the court process on children, over the past quarter century there have been substantial reforms in the law and the administration of justice. The law now better reflects what is known about the competency of child witnesses, as well as about their vulnerabilities. The paper includes a review of legislation and leading precedents, and a summary of the responses of Canadian judges to a survey about the most recent legislative reforms. The case law and survey reveal that judges are generally supportive of the reforms.

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)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0210.012
Scholarly communication0.0070.002
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.264
Teacher spread0.216 · 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 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

Citations8
Published2011
Admission routes2
Has abstractyes

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