Constitutional Cases (Pt 2) | Charter Equality, Indigeneity and Sentencing in R v Sharma 2022 SCC 39
Bibliographic record
Abstract
The 26th iteration of the Constitutional Cases conference was held on Friday, April 14, 2023. Osgoode Hall Law School’s Annual Constitutional Cases Conference, recognized as the leading constitutional law conference in Canada, brings together many highly respected constitutional scholars, lawyers, students, and experts for an insightful and practical analysis of the Supreme Court’s significant constitutional judgments of the past year.\nPlenary I: Charter Equality, Indigeneity and Sentencing in R v Sharma 2022 SCC 39 The path of section 15 doctrine has been a perennial topic at this conference. This year’s Sharma decision underscored existing divergent approaches on the court to section 15. Sharma, which includes a section 7 claim, also brought the Charter to questions looming large in contemporary discussion, including approaches to incarceration, the role of sentencing judges, the institutional competence of courts versus legislatures and last but not least legal responses to the impact of colonialism on contemporary lives.\nPanelists:\n00:04:55 Alana Robert, McCarthy Tétrault LLP 00:15:05 Professors Jennifer Koshan and Lisa Silver (and Jonnette Watson Hamilton), University of Calgary, Faculty of Law\n00:37:11 Professors Debra Parkes, UBC Allard School of Law and Sonia Lawrence, Osgoode Hall Law School Chair: Professor Bruce Ryder, Osgoode Hall Law School
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".