Conscientious Refusal and Health Professionals: Does Religion Make a Difference?
Bibliographic record
Abstract
Freedom of Conscience and Freedom of Religion should be taken to protect two distinct sets of moral considerations. The former protects the ability of the agent to reflect critically upon the moral and political issues that arise in her society generally, and in her professional life more specifically. The latter protects the individual's ability to achieve secure membership in a set of practices and rituals that have as a moral function to inscribe her life in a temporally extended narrative. Once these grounds are distinguished, it becomes more difficult to grant healthcare professionals' claims to religious exemptions on the basis of the latter than it is on the basis of the former. While both sets of considerations generate 'internal reasons' for rights to accommodation, the relevant 'external' reasons present in the case of claims of moral conscience do not possess analogues in the case of claims of religious conscience. However, the argument applies only to 'irreducibly religious' claims, that is to claims that cannot be translated into moral vocabulary. What's more, there may be reasons to grant the claims of religious persons to exemptions that have to do not with the nature of the claims, but with the beneficial effects that the presence of religious persons may have in the context of the healthcare institutions of multi-faith societies.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".