Selling conscience short: a response to Schuklenk and Smalling on conscientious objections by medical professionals
Bibliographic record
Abstract
In a thought-provoking paper, Schuklenk and Smalling argue that no right to conscientious objection should be granted to medical professionals. First, they hold that it is impossible to assess either the truth of conscience-based claims or the sincerity of the objectors. Second, even a fettered right to conscientious refusal inevitably has adverse effects on the rights of patients. We argue that the main problem with their position is that it is not derived from a broader reflection on the meaning and implications of freedom of conscience and reasonable accommodation. We point out that they collapse two related but distinct questions, that is, the subjective conception of freedom of conscience and the sincerity test. We note that they do not successfully show that the standard norm according to which exemption claims should not impose undue hardship on others is unworkable. We suggest that the main reason why arguments such as no one is forced to be a medical professional are flawed is that public norms should not constrain citizens to choose between two of their basic rights unless it is necessary. In fine, Schuklenk and Smalling, who see conscience claims as arbitrary dislikes, sell freedom of conscience short and forego any attempts at balancing the competing rights involved. We maintain the authors neglect that most of legal reasoning is contextual and that the blanket restriction of healthcare professionals' freedom of conscience is disproportionate.
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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 | 0.033 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.046 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.076 | 0.092 |
| 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".