Distinguishing Deference from Deferment: Assisted Suicide Is the Wrong Response
Bibliographic record
Abstract
In this article, I examine the practice of assisted suicide. I aim to do so by, first, briefly sketching a description of the current context with a particular focus on Canadian law and the prominent role that autonomy plays there. I point to resources from within the Roman Catholic tradition that serve as a counterbalance to the focus on autonomy. Second, given the prevalence of appeals to autonomy in the lives of practitioners and patients and how claims of autonomy influence considerations associated with assisted suicide, I describe the account of autonomy that is operative in these discussions and sketch an account of deference that should replace the commonplace account of autonomy. Finally, having considered popular appeals to medical assistance in dying, I ask whether cases of assisted suicide are best thought of as nonmedical cases and what appeals to traditions of thought outside of explicitly medical professions have to add to this discussion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.417 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".