Reference Re Genetic Non-Discrimination Act: How to Make Space for Some Certainty
Bibliographic record
Abstract
In the Reference re Genetic Non-Discrimination Act (Reference) the Supreme Court of Canada divided three ways, reproducing the divisions from the Reference re Assisted Human Reproduction Act (AHRA), decided a decade earlier. AHRA did not provide a majority statement of the rule for determining what constitutes a valid exercise of the section 91(27) criminal law power. Neither did the Reference. As a consequence, uncertainty in this area of the law persists. This article suggests arguments that, if adopted, would resolve this uncertainty. Part I summarizes the Reference, including the three sets of reasons written by Karakatsanis J., Moldaver J. and Kasirer J., respectively. Part II is organized around three spatial metaphors: the relationship of parts to the whole, breadth, and line-drawing. Part II begins by addressing an apparent disagreement in the federalism jurisprudence and in the Reference about the proper order for pith and substance analyses, when a part of an act is at issue. I argue that in some cases it is necessary to interpret an act as a whole before assessing its parts. Part II then turns to disagreements in the Court about the breadth of the criminal law power. I argue that Karakatsanis J.’s expansive interpretation places in jeopardy federalism principles and that Kasirer J.’s criticisms of that interpretation were justified. Part II concludes by examining a disagreement between Kasirer and Karakatsanis JJ. about whether the test for validity under the criminal law power should include a line-drawing exercise. I argue that this relatively narrow disagreement reveals a deeper debate about the appropriate role of courts in adjudicating disputes about the criminal law power. I conclude that Kasirer J.’s position flows from an understanding of the judicial role that is consistent with the broader federalism jurisprudence.
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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 | 0.051 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.075 |
| Scholarly communication | 0.024 | 0.048 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.027 | 0.038 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".