Constitutionalism and the Genetic Non-Discrimination Act Reference
Bibliographic record
Abstract
In the July 10, 2020 decision in Reference re Genetic Non-DiscriminationAct (GNDA Reference),1 the Supreme Court of Canada arrived at a complex three-to-two-to-four outcome, with a slim five-justice majority in two separate judgments upholding challenged portions of the federal Genetic Non-Discrimination Act (GNDA)2 as a valid exercise of Parliament’s criminal law power. The legislation, which some thought fundamentally oriented to the goal of preventing genetic discrimination, seemed to have attractive policy objectives, though we will ultimately suggest that the form of the legislation was not entirely in keeping with these aims. While it may have appeared pragmatically attractive to uphold the legislation, we suggest that the majority’s decision to do so comes at great cost to basic federalism principles, to legal predictability, and to prospects for well-informed intergovernmental cooperation. We argue that the courts must properly confine the effects of the GNDA Reference in accordance with established principles on the treatment of fragmented judicial opinions. We also argue that the courts must take significant steps to ensure that federalism jurisprudence remains well-grounded in legal principle, without the actual or apparent influence of extra-legal policy considerations[...]
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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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.033 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".