Medically assisted suicide: Recent jurisprudence and the challenges for law reform.
Bibliographic record
Abstract
Decisions on the issue of medically assisted suicide were delivered within a two-year period by the Supreme Court of Ireland (Fleming v Ireland [2013] IESC 19), the Supreme Court of Canada (Carter v Canada (Attorney General) [2015] 1 SCR 331; 2015 SCC 5), the High Court of South Africa (Stransham-Ford v Minister of Justice and Correctional Services 2015 (4) SA 50; [2015] 3 All SA 109; [2015] ZAGPPHC 230 (GP)), and the High Court of New Zealand (Seales v Attorney-General [2015] 3 NZLR 556; [2015] NZHC 1239). This editorial scrutinises the jurisprudence generated by the decisions, identifies their ramifications and argues that it is likely that the combination of the carefully constructed judgments, together with their reception by the legal, medical and general communities, will lead to an increasing impetus for end-of-life law reform in many countries. It reviews the June 2016 report of the Legal and Social Issues Committee of the Legislative Council of the Victorian Parliament as an example of such reform initiatives. The challenge for those who wish to construct such changes to the law is to fashion legislative regimes which provide adequate protection to patients, as well as to the life-saving culture of medicine, and to safeguard dignity but ensure that respect for the quality of life is not eroded by pressures to end lives that some regard as no longer having value.
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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 | 0.055 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.088 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.058 | 0.055 |
| Insufficient payload (model declined to judge) | 0.002 | 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".