Bibliographic record
Abstract
In medically advanced countries, voluntary euthanasia and assisted suicide are hotly debated issues across all strata of society. Legislation permitting the practice of hastening your own death with help from a third party has now been passed or is pending in a number of US states, Canada, Switzerland, Belgium, Luxembourg, and the Netherlands. Parliamentary bills which would legalise a form of assisted suicide were also debated by the UK and Scottish parliaments in 2015. The beliefs that people hold about the practice are hugely influenced by their own first-hand experiences of illness and death (Hendry et al., 2013; Judd and Seale, 2011), as much as by any pre-existing moral code stemming from religious and cultural teachings or otherwise. The social and legal sanctioning of voluntary euthanasia and assisted suicide may divide opinion, but the fact that so many people appear to hold and are prepared to vocalize strong opinions about an issue relating to death and dying is unusual, given that a number of social taboo which still operates to regulate discussions about the end of life more generally (Walter, 1991). In many ways, the high profile occupied by the voluntary euthanasia and assisted suicide debate has facilitated public discussion of broader issues to do with the ways in which people die in the twenty-first century. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".