Getting Real About Killing and Allowing to Die: A Critical Discussion of the Literature
Bibliographic record
Abstract
The moral significance of the distinction between killing and allowing to die has played a key role in debates about euthanasia and physician assisted suicide. Since the withdrawal of life-sustaining treatment is held as morally permissible in the medical community, it follows that if there is no morally significant difference between killing and allowing to die, then there is no morally significant difference between withdrawing life-sustaining treatment or administering a lethal injection to end a patient’s life. Consistency then requires that voluntary active euthanasia (VAE) is also morally permissible. The debates over whether the distinction is morally significant have carried on for decades with little hope of consensus. We begin by surveying the literature to identify common argumentative strategies used in defending or rejecting the distinction’s significance. We observe, based on our review, that many of these strategies operate in ways that are conceptually removed from the concrete clinical situation of physicians involved in practices that lead to patient death (by withdrawal of treatment or VAE). We conclude by arguing for a novel way of moving the debate forward indicated by our reading of the literature, namely, by paying careful attention to the moral experience of physicians involved in end-of-life interventions to understand how they experience these practices. Exploring physician experience can reveal how the distinction may or may not be useful for moral deliberation and can provide the needed context to theorize about the distinction in a more empirically informed and practically useful way.
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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 | 0.053 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.026 | 0.028 |
| Science and technology studies | 0.014 | 0.041 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".