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Record W2325496734 · doi:10.5840/bpej200019123

Peter Miller, Axiology and Environmental Ethics

2000· article· en· W2325496734 on OpenAlexaff
Philip MacEwen

Bibliographic record

VenueBusiness and Professional Ethics Journal · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Ecology, and Ethics
Canadian institutionsYork University
Fundersnot available
KeywordsAxiologyMillerApplied philosophyEnvironmental ethicsPhilosophySociologyEpistemologyEcologyBiology

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.059
GPT teacher head0.369
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2000
Admission routes1
Has abstractyes

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