Against the accommodation of subjective healthcare provider beliefs in medicine: counteracting supporters of conscientious objector accommodation arguments
Bibliographic record
Abstract
We respond in this paper to various counter arguments advanced against our stance on conscientious objection accommodation. Contra Maclure and Dumont, we show that it is impossible to develop reliable tests for conscientious objectors' claims with regard to the reasonableness of the ideological basis of their convictions, and, indeed, with regard to whether they actually hold they views they claim to hold. We demonstrate furthermore that, within the Canadian legal context, the refusal to accommodate conscientious objectors would not constitute undue hardship for such objectors. We reject concerns that refusing to accommodate conscientious objectors would limit the equality of opportunity for budding professionals holding particular ideological positions. We also clarify various misrepresentations of our views by respondents Symons, Glick and Jotkowitz, and Lyus.
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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 | 0.079 | 0.237 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.061 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 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".