Child Non-Voluntary Euthanasia: When Quality Trumps Quantity
Bibliographic record
Abstract
For decades, the medical community has debated how best to alleviate the pain and suffering of patients while respecting moral values and judgement. Throughout these years, the field of medicine has birthed new methods and technology to enhance palliative care. However, at times, the patients’ conditions only worsen and the use of treatments and medications are to no avail. In these circumstances, euthanasia provides an alternative solution to the suffering of patients and their families. Euthanasia is defined as “knowingly and intentionally performing an act that is explicitly intended to end another person’s life.” 1 It is currently legal in only eight jurisdictions around the globe. However, in late 2013, Bill 52 by the Québec legislature proposed to decriminalize euthanasia as a medical aid for death. Not only has this initiated further debate on the topic in Canada, but it may encourage other governments to consider implementing euthanasia. This opinion piece discusses how legalizing euthanasia would serve the interests of the patient and healthcare system, with a particular focus on child euthanasia
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 | 0.041 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".