Does the Attorney General Have a Duty to Defend Her Legislature’s Statutes? A Comment on the <i>Reference Re Genetic Non-Discrimination Act</i>
Bibliographic record
Abstract
The Reference Re Genetic Non-Discrimination Act was unusual because the Attorney General for Canada argued that federal legislation was unconstitutional. In this comment, I explore the implications of this choice for the role of the Attorney General and her relationship with Parliament. I argue that the Attorney General has a duty not to defend legislation, including legislation that began as a private member’s bill, that she reasonably believes to be unconstitutional – and that if Parliament wants to defend such legislation, it should do so itself instead of relying on the Attorney General. If Parliament does not do so, the Attorney General should support the appointment of amicus. However, where the Attorney General advises Parliament during the legislative process that a bill is unconstitutional, Parliament’s rejection of that advice is legally irrelevant and not wrongful. That rejection should nonetheless prompt the Attorney General to resign, if indeed she is the lawyer to the legislature.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.075 | 0.050 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".