Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".