Deciding on Death: Rodriguez, Carter, and Medically Assisted Dying in Canada
Bibliographic record
Abstract
Should Canadians have the right to medical assistance in dying? That question has galvanized debate since the early 1990s, when Sue Rodriguez unsuccessfully challenged the criminalization of assisted dying. The Supreme Court of Canada subsequently reversed its position in a 2015 case initially brought by the family of Kay Carter, who had travelled outside the country for access to an assisted death. Deciding on Death provides a comprehensive history of medical assistance in dying (MAiD) in Canadian law through a close analysis of the Rodriguez and Carter decisions. It also traces the political and legislative developments before and after those landmark cases. The controversy is ongoing, with unresolved questions about medical assistance for mature minors, those with mental illness, and persons making advance requests. However, Carter clarified the circumstances under which the court was willing to overrule its own decisions and elucidated the Charter right to life, liberty, and security of the person. Legalization of medically assisted dying has finally given many Canadians with incurable medical conditions that cause them intolerable suffering the ability to choose the manner and timing of their death. Over fifteen thousand people per year now pursue that option. This timely book explains how we got here and the decisions that still lie ahead. Deciding on Death illuminates a controversial and deeply personal topic for scholars and students of political science, law, and society, as well as for politicians, medical practitioners, and a wider readership, as these legal decisions will affect everyone’s consideration of their own end of life.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.049 | 0.019 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".