Zones of consensus and zones of conflict: Questioning the common morality presuppositions in bioethics
Bibliographic record
Abstract
ABSTRACT. Many bioethicists assume that morality is in a state of wide reflec-tive equilibrium. According to this model of moral deliberation, public policymaking can build upon a core common morality that is pretheoretical and provides a basis for practical reasoning. Proponents of the common morality approach to moral deliberation make three assumptions that deserve to be viewed with skepticism. First, they commonly assume that there is a universal, transhistorical common morality that can serve as a normative baseline for judg-ing various actions and practices. Second, advocates of the common morality approach assume that the common morality is in a state of relatively stable, ordered, wide reflective equilibrium. Third, casuists, principlists, and other pro-ponents of common morality approaches assume that the common morality can serve as a basis for the specification of particular policies and practical recom-mendations. These three claims fail to recognize the plural moral traditions that are found in multicultural, multiethnic, multifaith societies such as the United States and Canada. A more realistic recognition of multiple moral traditions in pluralist societies would be considerably more skeptical about the contributions that common morality approaches in bioethics can make to resolving conten-tious moral issues.
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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 | 0.080 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.017 | 0.137 |
| Scholarly communication | 0.023 | 0.044 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".