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Record W2086171972 · doi:10.1111/1467-9930.00139

The Underrepresentation of Indigenous Peoples on Canadian Jury Panels

2003· article· en· W2086171972 on OpenAlexaboutno aff
Mark A. Israel

Bibliographic record

VenueLaw & Policy · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicJury Decision Making Processes
Canadian institutionsnot available
Fundersnot available
KeywordsJuryIndigenousSupreme courtLawJury selectionPolitical scienceLegislationEconomic JusticeCommissionPoliticsCommon lawAdministration (probate law)Jury trial

Abstract

fetched live from OpenAlex

Under common law, Canadian jury panels, or arrays, are supposed to be broadly representative. In the early 1980s, the Law Reform Commission and, in the early 1990s, the Supreme Court claimed that provincial legislation virtually guaranteed that this was the case. However, evidence presented to various provincial and federal commissions and a series of court cases has pointed to the continuing underrepresentation of Indigenous Canadians resulting from both the content and the administration of provincial laws. In this article, I examine evidence of underrepresentation and review various political and legal attempts to challenge bias in out‐of‐court selection. I suggest that contemporary practices in some jurisdictions have not consistently provided a representative jury pool or panel. As a result, the jury selection process has not always appeared to offer justice to Indigenous people and, in doing so, may not have served the Canadian legal system well.

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)0.053
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.122
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.004
Science and technology studies0.0150.006
Scholarly communication0.0050.001
Open science0.0020.006
Research integrity0.0020.002
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 head0.056
GPT teacher head0.392
Teacher spread0.337 · 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 designObservational
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

Citations7
Published2003
Admission routes1
Has abstractyes

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