Assisted dying/assisted suicide in the UK: an idea whose time has come?
Bibliographic record
Abstract
Assisted dying/assisted suicide (AD/AS) has emerged as an important policy issue across the world, with an increasing number of jurisdictions legalizing the practice in recent years. In this context, the article by Bache published earlier this year (2025) in Politics & Policy applied agenda-setting theory to consider why AD/AS has not been legalized in the United Kingdom (UK) despite consistently high levels of public support. In addition, it considered the prospects for policy change and identified the factors that made policy change likely in the near future. Shortly after this article was published, a Parliamentary vote indicated that AD/AS would likely become available in the UK within a few years. While the original article outlined a range of factors that explained this probable policy change, it offered very limited space to consider developments in other jurisdictions and to reflect on what might be learned about the nature, pace, and timing of developments in the UK from these cases. Therefore, this additional piece is intended to extend and update the previous article in these areas. The discussion looks at the three cases that have featured most prominently in UK debates, and which have legalized through different routes—Oregon (referendum), Canada (courts), and the Netherlands (legislature)—thus drawing attention to systemic differences that provide insights for the UK case. I situate the discussion within the literature on morality policy, taking the comparativist “Two Worlds” approach as its starting point.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.030 |
| Scholarly communication | 0.027 | 0.040 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".