On the Naturalistic Fallacy: A Conceptual Basis for Evolutionary Ethics
Bibliographic record
Abstract
In debates concerning evolutionary approaches to ethics the Naturalistic Fallacy (i.e., deriving values from facts or “ought” from “is”) is often invoked as a constraining principle. For example, Stephen Jay Gould asserts the most that evolutionary studies can hope to do is set out the conditions under which certain morals or values might have arisen, but it can say nothing about the validity of such values, on pain of committing the Naturalistic Fallacy. Such questions of moral validity, he continues, are best left in the domain of religion. This is a common critique of evolutionary ethics but it is based on an insufficient appreciation of the full implications of the Naturalistic Fallacy. Broadly conceived, the Naturalistic Fallacy rules out any attempt to treat morality as defined according to some pre-existent reality, whether that reality is expressed in natural or non-natural terms. Consequent to this is that morality must be treated as a product of natural human interactions. As such, any discipline which sheds light on the conditions under which values originate, and on the workings of moral psychology, may play a crucial role in questions of moral validity. The authors contend that rather than being a constraint on evolutionary approaches to ethics, the Naturalistic Fallacy, so understood, clears the way, conceptually, for just such an approach.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.056 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".