No conscientious objection without normative justification: Against conscientious objection in medicine
Bibliographic record
Abstract
Most proponents of conscientious objection accommodation in medicine acknowledge that not all conscientious beliefs can justify refusing service to a patient. Accordingly, they admit that constraints must be placed on the practice of conscientious objection. I argue that one such constraint must be an assessment of the reasonability of the conscientious claim in question, and that this requires normative justification of the claim. Some advocates of conscientious object protest that, since conscientious claims are a manifestation of personal beliefs, they cannot be subject to this kind of public justification. In order to preserve an element of constraint without requiring normative justification of conscientious beliefs, they shift the justificatory burden from the belief motivating the conscientious claim to the condition of the patient being refused service. This generally involves a claim along the lines that conscientious refusals should be permitted to the extent that they do not cause unwarranted harm to the patient. I argue that explaining what would constitute warranted harm requires an explanation of what it is about the conscientious claim that makes the harm warranted. 'Warranted' is a normative operator, and providing this explanation is the same as providing normative justification for the conscientious claim. This shows that resorting to facts about the patient's condition does not avoid the problem of providing normative justification, and that the onus remains on advocates of conscientious objection to provide normative justification for the practice in the context of medical care.
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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 | 0.051 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.112 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.044 | 0.039 |
| 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".