The Rhetoric of Assisted Suicide and Euthanasia (‘Medical Assistance in Dying’)
Bibliographic record
Abstract
A minority of countries or parts of countries have thus far accepted the legal practice of Medical Assistance in Dying (MAiD) (euthanasia and assisted suicide), but legalising MAiD is expanding worldwide. More countries are debating legalisation of euthanasia or assisted suicide, but the nature of laws and legal practices vary greatly and both ethical and empirical assessments of current practices are the subject of much controversy. We examine the premises and evidence in the rhetoric of assisted suicide and euthanasia. We illustrate the trend with the rationale and political concerns that led to the legalisation of euthanasia in Quebec as “Medical Assistance in Dying” (MAiD), and its subsequent expansion in Canada to include persons who do not suffer from a terminal illness, including persons who suffer only from a mental illness. The values of autonomy, “dying with dignity“ and their ethical and legal bases for justifying MAiD are critically analysed. The implications of practicing euthanasia, as opposed to assisted suicide are discussed, as well as proposals for a duty to die in some circumstances. We conclude by proposing that besides debating the legal, moral and practical concerns with MAiD, we should also focus on the psychological roots of our fears and ways to reduce those fears in individuals and societies.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".