WHICH NATURALISM FOR BIOETHICS? A DEFENSE OF MODERATE (PRAGMATIC) NATURALISM
Bibliographic record
Abstract
There is a growing interest in various forms of naturalism in bioethics, but there is a clear need for further clarification. In an effort to address this situation, I present three epistemological stances: anti-naturalism, strong naturalism, and moderate pragmatic naturalism. I argue that the dominant paradigm within philosophical ethics has been a form of anti-naturalism mainly supported by a strong 'is' and 'ought' distinction. This fundamental epistemological commitment has contributed to the estrangement of academic philosophical ethics from major social problems and explains partially why, in the early 1980s, 'medicine saved the life of ethics'. Rejection of anti-naturalism, however, is often associated with strong forms of naturalism that commit the naturalistic fallacy and threaten to reduce the normative dimensions of ethics to biological imperatives. This move is rightly dismissed as a pitfall since ethics is, in part, a struggle against the course of nature. Rejection of naturalism has drawbacks, however, such as deterring bioethicists from acknowledging the implicit naturalistic epistemological commitments of bioethics. I argue that a moderate pragmatic form of naturalism represents an epistemological position that best embraces the tension of anti-naturalism and strong naturalism: bioethics is neither disconnected from empirical knowledge nor subjugated to it. The discussion is based upon historical writings in philosophy and bioethics.
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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 | 0.035 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.070 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".