Bibliographic record
Abstract
A THEORY OF VALUES Although this century has produced more, and more varied, ethical and metaethical theory than any other, even our more educated and intelligent people are simply embarrassed when asked how they justify the value choices and commitments they make. We could well use a credible superstructure of facts and concepts within which we might carry on intersubjective and intercultural discussions of value differences, discussions that would offer some reasonable prospect of eventual agreement. What follows here is the outline of such a superstructure, a biologically based, naturalistic, species-universal, and prescriptive value theory (McShea, 1990). The theory is designed to answer such questions as these: Can a value statement be true? If so, in what sense and for whom? How can a value statement have prescriptive force? The theory is an update of a philosophical ethical tradition that includes Aristotle, Spinoza, and especially Hume, who set forth a naturalistic, biologically based account of human nature and the meaning of life. Modern human nature theorists, with whom we would expect to find much common ground include Mackie (1977), Murphy (1982), Ruse (1986), and occasionally Midgley (1978), although most probably would not concur in the understanding of Hume on which the theory is based. Six Value Bases As the greatest success of science is not the discovery of this or that truth about things, but the learned ability to think scientifically, so the reward for the study of good value theory is not the discovery of moral laws or truths, but the habit of leading an examined life (which is morality itself).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.037 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".