Bibliographic record
Abstract
John Arras argues against the legalization of physician-assisted suicide and active euthanasia on the basis of social costs that he anticipates will result from legalization. Arras believes that the legalization of highly restricted physician-assisted suicide will result in the legalization of active euthanasia without special restrictions, a prediction I grant for the sake of argument. Arras further anticipates that the practices of physician-assisted suicide and euthanasia will be abused, so that many patients who engage in these practices will lose out as a result. He refers to these losses as social costs to legalization. But the social costs at play in typical public policy debates are borne by individuals other than the agent who engages in the controversial activity, specifically by people who cannot be held responsible for enduring those costs. Even if plausible interpretations of Arras’ predictions about the abuse of the practice are granted, legalization of physician-assisted suicide or euthanasia brings no social costs of this latter sort. For this reason, and also because a ban on euthanasia is unfair to those who would profit from it, the losses in utility brought about by legalization would have to be very great to justify a ban.
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 | 0.037 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.077 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.032 | 0.037 |
| Insufficient payload (model declined to judge) | 0.005 | 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".