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Record W3121420720 · doi:10.60082/2563-8505.1160

Ignoring the Golden Principle of Charter Interpretation?

2008· article· en· W3121420720 on OpenAlexaboutno aff
David M Tanovich

Bibliographic record

VenueSupreme Court law review · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSupreme courtCharterLawInterpretation (philosophy)CertiorariPolitical scienceJudicial interpretationSociologyOriginal jurisdictionPhilosophy

Abstract

fetched live from OpenAlex

One consistent and disturbing trend since the birth of the Charter in 1982 is that race has been and continues to be, with a few notable exceptions, erased from the factual narratives presented to the Supreme Court of Canada and from the constitutional legal rules established by the Court in criminal procedure cases. Understanding the etiology of this erasing is not easy. In earlier pieces, the author has explored the role of trial and appellate lawyers. This paper focuses on principles of judicial review and the failure of the Supreme Court to consistently consider the impact of the constitutional rules it creates or interprets on Aboriginal and racialized communities. What makes the silence so problematic is that the Supreme Court gave itself the tool in 2001 to address part of the identified problem when it established an anti-racism principle of Charter interpretation in R. v. Golden. This paper seeks to address a number of questions focused on the legacy of Golden. What is the origin and content of the Golden principle of judicial review? What is the evidence from subsequent cases and academic commentary that this is indeed an accepted principle of constitutional interpretation? What cases from the 2007 Supreme Court term would have benefited from a critical race analysis? in particular, how would factoring in Golden have affected the Court’s analysis in R. v. Clayton? And, finally, how should the Golden principle be applied in future cases?

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.947
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.349
Teacher spread0.291 · 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 teacher head, 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

Citations1
Published2008
Admission routes1
Has abstractyes

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