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Record W3123265210 · doi:10.60082/2563-8505.1128

The Charter of Whiteness: Twenty-Five Years of Maintaining Racial Injustice in the Canadian Criminal Justice System

2008· article· en· W3123265210 on OpenAlexaboutno aff
David M Tanovich

Bibliographic record

VenueSupreme Court law review · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsnot available
Fundersnot available
KeywordsInjusticeCharterPolitical scienceCriminal justiceEconomic JusticeLawRacial profilingLegislationGovernment (linguistics)Critical race theoryMass incarcerationRace (biology)CriminologyRacismSociologyGender studies

Abstract

fetched live from OpenAlex

This paper examines the impact of the Canadian Charter of Rights and Freedoms on racial injustice in the Canadian criminal justice system. While there have been modest gains, we have seen very little systemic change over the Charter’s 25-year life. We continue to incarcerate Aboriginals and African Canadians at alarming rates, racial profiling at our borders and in our streets continues to flourish, and the federal government continues to propose legislation that will further entrench the problem. Why is this happening? is there any hope for the future? these are some of the fundamental questions explored. of course, some might say that it is simply naive to think that constitutional litigation can make a difference when dealing with structural and systemic problems. This larger philosophical question is addressed in Part II. Part III sets out and defends the the sis that it is not so much the Charter that is the problem but rather those who apply and interpret it. Racial justice has not had a chance to grow over the last 25 years because there has been a significant failure of trial and appellate lawyers to engage in race talk in the courts and a failure of the judiciary to adopt appropriate critical race standards when invited to do so. Engaging in race talk and developing critical race standards are critical because colourblind due process standards are working disproportionately to the disadvantage of racialized groups. There is reason though to be optimistic. Criminal lawyers and judges are committed to the pursuit of justice and with a greater understanding of what needs to be done and commitment to getting it done, we may begin to see some significant change.

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)0.018
metaresearch head score (Gemma)0.020
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: Empirical · Consensus signal: none
Teacher disagreement score0.149
Threshold uncertainty score0.987

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0700.039
Scholarly communication0.0160.005
Open science0.0030.007
Research integrity0.0040.008
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 head0.059
GPT teacher head0.332
Teacher spread0.273 · 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
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

Citations6
Published2008
Admission routes1
Has abstractyes

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