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Record W2039586281 · doi:10.7202/1026678ar

Thinking Globally, Acting Locally: Partiality, Preferences and Perspective

2014· article· en· W2039586281 on OpenAlexvenueno aff
Graham Oddie

Bibliographic record

VenueLes ateliers de l éthique · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Perspective (graphical)EpistemologyPreferenceIntrinsic value (animal ethics)Positive economicsNatural (archaeology)Mathematical economicsSpecies richnessSubject (documents)Computer scienceSociologyMathematicsEconomicsPhilosophyLawMicroeconomicsArtificial intelligencePolitical scienceEcologyStatisticsHistory

Abstract

fetched live from OpenAlex

A rather promising value theory for environmental philosophers combines the well-known fitting attitude (FA) account of value with the rather less well-known account of value as richness . If the value of an entity is proportional to its degree of richness (which has been cashed out in terms of unified complexity and organic unity ), then since natural entities, such as species or ecosystems, exhibit varying degrees of richness quite independently of what we happen to feel about them, they also possess differing degrees of mind-independent and subject-independent value. In particular, their value is not dependent on the desires or preferences of humans. The fitting attitudes account of value, at least as it is standardly developed, demands isomorphic evaluative responses on the part of all valuers. In particular, it entails that all valuers should have isomorphic preferences. But this seems absurd. I consider three different strategies with which the fitting attitude theorist can deflect this challenge. The first makes use of an account of non-standard value relations in terms of permissible preference orderings. The second appeals to value appearances and the associated notions of value distance and value perspective. The third involves an account of the ultimate bearers of value as properties, rather than as propositions or states of affairs. These strategies are not all mutually incompatible. While it isn’t possible to combine the first and second strategies, it is possible to combine the first and third strategies, and also to combine the second and the third.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.252
Teacher spread0.233 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2014
Admission routes1
Has abstractyes

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