A TIME TO LIVE AND A TIME TO DIE - WHO DECIDES? CAN "THE GOOD DEATH" BE ACHIEVED WITHOUT THE NEGATIVE REPERCUSSIONS OF LEGALIZING EUTHANASIA AND ASSISTED SUICIDE FOR CANADA?
Bibliographic record
Abstract
T he 1973 science fiction movie Soylent Green is set in New York City in 2022. Policeman Sol Roth (played by Edward G. Robinson) decides he cannot live with his knowledge about the Soylent Corporation (he discovers they are turning human remains into food and deceiving the people, to boot) and opts to “go home” – he registers at a clinic for his own death.1 A far-fetched sci-fi flick to be sure, but end-of-life decisions today are most assuredly not confined to the silver screen. There is noise to allow for more choices in public policy – even in death. From the Sue Rodriguez2 and Robert Latimer3 cases in Canada, Terri Schiavo4 in the U.S., legalized euthanasia in Holland5 and the state of Oregon6 as well as a series of private member’s bills in the House of Commons, euthanasia is a topic under discussion.7 Must legalization of euthanasia and assisted suicide be part of Canada’s future or is there a better way?
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.032 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.014 | 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".