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Record W2102630478 · doi:10.1177/1077801208317291

And Justice for All?

2008· article· en· W2102630478 on OpenAlexaffabout
Arielle Dylan, Cheryl Regehr, Ramona Alaggia

Bibliographic record

VenueViolence Against Women · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCriminal justiceLegislatureEconomic JusticeCriminologyRace (biology)Qualitative researchProcedural justicePoison controlAdministration of justicePolitical scienceSociologyPsychologyLawMedicineMedical emergency

Abstract

fetched live from OpenAlex

Concern for the recognition, support, and rights of victims within the criminal justice system has grown in recent years, leading to legislative and procedural changes in the administration of justice that have improved the experiences of victims. What is not clear is whether all victims have benefited from changes in the system regardless of race and social class. This study investigates the experiences Aboriginal people who are victims of sexual violence have with the Canadian criminal justice system. The authors seek to explore perspectives about their encounters with the judicial system from the point of first contact with the police through involvement with the court and community service providers, utilizing grounded theory qualitative methodology. They conclude that race is a key determinant in the manner in which a victim will be perceived by the people in the justice system and the manner in which the victim will approach the judicial 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 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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.207
Threshold uncertainty score0.412

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.017
Scholarly communication0.0070.006
Open science0.0010.006
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0190.003

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 head0.049
GPT teacher head0.338
Teacher spread0.289 · 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

Citations48
Published2008
Admission routes2
Has abstractyes

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