An Opportunity for Equality: Kokopenace and Nur at the Supreme Court of Canada
Bibliographic record
Abstract
The last thirty years have seen the tentative emergence of an expanded conception of criminal law, a conception which takes account of the constitutional value of equality. Much of my scholarship argues that equality offers a jurisprudential window into the undeniable fact that those subjected to criminal law are disproportionately the victims of structural inequality, a fact that is routinely invisible in much theorizing about criminal law. The fall term of 2014 offered the Supreme Court an opportunity to incorporate equality analysis in two important criminal law cases. In Kokopenace, the Court will consider the scope of an Aboriginal defendant’s constitutional right to a representative jury roll and in Nur, the Court will rule on the constitutionality of mandatory minimum sentences for the offence of gun possession. This essay identifies, in advance of the Supreme Court’s ruling, the jurisprudential moments where substantive equality values could be influential. Whether and how the Supreme Court is prepared to identify an equality rights violation, or name equality as a relevant analytical factor, is difficult to predict. Historically, the Court has preferred to ground its criminal law analysis in more familiar criminal law concepts such as fundamental justice, fair trial, and privacy. There are understandable reasons for this reluctance related to judicial economy, but also legitimately related to the deep complexities associated with identifying, measuring and balancing the different and sometimes incommensurate equality claims presented by particular issues. In my view, these cases offer the Court two discrete criminal justice opportunities to affirm its commitment to substantive equality in a larger political context where the systemic and structural dimensions of criminal justice issues are deliberately and explicitly undervalued.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.065 | 0.019 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".