Bibliographic record
Abstract
Peter Miller has devoted much of his philosophical career to denying the metaphysical view that nature is value-free, aside from whatever use or exchange value human beings think it has for them. In particular, he has challenged the psychologizing of values according to which, were there no minds or conscious experience, there would be nothing of value in the world. This is not to say that Miller is against the position that some values are psychological and that psychological theories of value have embodied many important developments in our self-discovery. He readily acknowledges that psychological theories of value can teach us to respect important features of the mental life we share or aspire to, including dimensions of intelligence, feeling, imagination, sympathy and will. They can also teach respect for the particular nuances of these dimensions that constitute us as distinctive personalities and free us for responsible self determination informed by personal experience.1 By themselves, however, psychological theories of value place disturbing limits on the realms of value and the value of nature in particular. One of the clearest examples of this is Locke's claim: Land that is left wholly to Nature, that hath no improvement of Pasturage, Tillage, or Planting, is called, as indeed it is, wast[e] and we shall find the benefit of it amount to little more than nothing.2 Indeed, Locke calculated that, of the products of the earth that
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".