Bibliographic record
Abstract
Analytic moral philosophy's strong divide between empirical and normative restricts facts to providing information for the application of norms and does not allow them to confront or challenge norms. So any genuine attempt to incorporate experience and empirical research into bioethics--to give the empirical more than the status of mere 'descriptive ethics'--must make a sharp break with the kind of analytic moral philosophy that has dominated contemporary bioethics. Examples from bioethics and science are used to illustrate the problems with the method of application that philosophically prevails in both domains and with the conception of rationality that underlies this method. Cues from how these problems can be handled in science then introduce summaries of richer, more productive naturalist and constructivist accounts of reason and normative knowledge. Liberated by a naturalist approach to ethics and an enlarged conception of rationality, empirical work can be recognized not just as essential to bioethics but also as contributing to normative knowledge.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".