Safeguarding Provincial Autonomy from the Supreme Court's New Federal Paramountcy Doctrine: A Constructive Role for the Intention to Cover the Field Test?
Bibliographic record
Abstract
This paper reviews and critiques the Supreme Court of Canada's consideration of the constitutional law doctrine of federal paramountcy, in particular its willingness to broaden the circumstances in which the doctrine applies. Preservation of a high degree of provincial autonomy has been a consistent theme of federalism jurisprudence in Canada. Since taking over from the Judicial Committee of the Privy Council as the court of last resort for Canada, the Supreme Court has allowed federal and provincial legislation to overlap in a broad range of different areas. The Court's recent approach to the paramountcy doctrine could therefore potentially lead to significant inroads into provincial autonomy. This paper has four objectives: (1) to trace the evolution of the paramountcy doctrine from 1982 to 2007; (2) to summarize the Court's current extended understanding of the paramountcy doctrine's reach and its practical and theoretical implications; (3) to provide critical scrutiny of that understanding from the perspectives of constitutional principle and constitutional policy; and (4) to suggest a new way of thinking about the doctrine, which accepts the validity of its recent extension, but attempts to mitigate the potential damage that the extension may cause to provincial autonomy.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| 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".