Assisted Suicide and Euthanasia: Language Lost in MAiD
Bibliographic record
Abstract
For most of Canada’s approximately 40-year debate on medically assisted death, euthanasia and assisted suicide were considered distinct issues. Yet in 2016 their ethical, psychological, and practical differences were effectively disregarded when the two acts were grouped together in the legislation under the umbrella term “Medical Assistance in Dying” (MAiD). The lack of distinction under the law of the two terms ignores important ethical considerations from the MAiD practitioners’ perspective. Although the principle of respect for autonomy must remain central to the assessments of MAiD eligibility, it cannot be the only consideration. This paper examines the ethical considerations and principles that underlie decisions to provide MAiD through an analysis of the progress, and results, of the 40-year debate on assisted suicide and euthanasia.
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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 | 0.033 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.076 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.009 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 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".