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Record W4396683562 · doi:10.55496/frqq3028

"Prisoner Never Gave Me Anything For What He Had Done:" Aboriginal Voices in the Criminal Court

2007· article· en· W4396683562 on OpenAlexaffabout
Shelley A. M. Gavigan

Bibliographic record

VenueSocio-Legal Review · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsYork UniversityUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsCriminalizationScholarshipCriminologyCriminal lawLawAlienCriminal courtPolitical scienceSociologyInterpreterInternational lawPoliticsCitizenship

Abstract

fetched live from OpenAlex

Aboriginal people participated in different ways in the criminal process in the early years of the North-West Territories region of Canada: including, as accused persons, as Informants, and as witnesses. Their physical participation was often mediated by the police, Indian agents and sometimes their Chiefs. Their words were also mediated by interpreters, both linguistic and cultural, and their signatures invariably marked as “X” on their depositions. Scholarship that has examined the relationship of Aboriginal peoples to the criminal law has tended to interrogate the criminalization and moral regulation strategies implicit in the process of colonization and domination of the First Peoples. This paper will discuss less visible aspects of the legalized processes of colonization: (1) the participation of Plains Cree, Saulteaux and Métis peoples, among others, whose traditional values and norms nonetheless seep through the handwritten, translated transcription and alien norms of the Canadian criminal court; and, (2) cases in which Aboriginal complainants who, notwithstanding their substantive inequality, invoked the criminal process to insist that those who wronged them also be punished in accordance with the principles of Canadian law.

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)0.004
metaresearch head score (Gemma)0.009
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 score0.642
Threshold uncertainty score0.721

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.386
Teacher spread0.359 · 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

Citations0
Published2007
Admission routes2
Has abstractyes

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