International Perspectives on Reforming End-of-Life Law
Bibliographic record
Abstract
This chapter identifies international trends in end-of-life law reform from analysing ten case studies of reform from the United Kingdom, the United States, Canada, Australia, the Netherlands and Belgium. A key finding is that law reform is more likely to succeed when supported by ‘good process’. This includes effective consultation with key stakeholders and engaging with experts. Social science evidence is also increasingly influential in both legislative and judicial reform, particularly in relation to how assisted dying systems can operate safely in practice. Other factors contributing to reform are the support or advocacy of key individuals or groups, shifts in community sentiment, and changes in political composition of parliaments. The chapter also concludes that law reform is ultimately a political exercise. Compromise is often required for a law to pass. This has implications for designing effective end-of-life law, pointing to the need for critical evaluation of both proposed laws and how existing laws operate in practice. The chapter concludes with reflections about the future of end-of-life law.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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".