Panel A: Lines in the Sand - GGPPA & Desautel | 25th Annual Constitutional Cases Conference
Bibliographic record
Abstract
This panel will discuss two significant cases decided in 2021 that speak to the subject of jurisdiction. From a series of perspectives, panelists will examine the division of powers analyses on offer in the References re Greenhouse Gas Pollution Pricing Act by looking historically at the case, more deeply into the recent jurisprudence on federalism, by analyzing the national concern doctrine, and posing important questions about climate change and the constitution. The panel will also discuss the important decision of R. v. Desautel, which considered the recognition of constitutionally protected Aboriginal Rights under s.35(1) rights beyond the borders of the Canadian state that arise based on the prior occupation of Aboriginal societies.\n3:02 Fenner Stewart, University of Calgary, Faculty of Law "The Great Case of Minimum National Standards"\n16:05 Allan Hutchinson, Osgoode Hall Law School\n25:40 Jean Leclair, Faculté de droit, Université de Montréal "’Tis a rock — a crag — a cape? A cape? say rather a peninsula!” The SCC’s Revisitation of the National Concern doctrine"\n39:26 Senwung Luk, OKTLaw "Are there geographical bounds to Van der Peet rights? A study of R v Desautel"\nChair: Emily Kidd White, Osgoode Hall Law School\nThis event was recorded on Friday, April 1, 2022 Hosted by Osgoode Hall Law School\nSponsored by LexisNexis and Osgoode Professional Development
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.022 | 0.013 |
| Insufficient payload (model declined to judge) | 0.075 | 0.013 |
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".