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Record W4294237747 · doi:10.1093/jaac/kpac043

The Art of Environmental Law, Governing with Aesthetics

2022· article· en· W4294237747 on OpenAlexaff
Jennifer Welchman

Bibliographic record

VenueJournal of Aesthetics and Art Criticism · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAestheticsEnvironmental lawSociologyEnvironmental ethicsLawPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Though nearly 400 pages, Benjamin Richardson’s The Art of Environmental Law, Governing with Aesthetics, will not tell you everything you always wanted to know about aesthetics and environmental law but were afraid to ask. What it will give you is a fascinating overview that is remarkably readable despite its considerable length. Richardson’s opening chapter explains that his objective is to show “how insights from aesthetics can enrich the study and understanding of environmental law.” (p. 5) Strictly speaking, what he draws upon are insights about aesthetics rather than from aesthetic theories, philosophical or otherwise. Richardson does occasionally draw upon philosophical texts, most frequently those of Allen Carlson and Glenn Parsons, Arnold Berleant, Yuriko Saito, and Emily Brady. But this is a work of applied aesthetics, aimed at an interdisciplinary audience. Indeed its greatest strength is its interdisciplinarity. Richardson draws on studies of environmental law and landscape management, biodiversity conservation, museum practices, advertising, ecotourism, environmental restoration, land art, and environmental activism, as well as philosophical environmental aesthetics. The result is a valuable resource for anyone interested in the many different avenues through which aesthetic values, broadly construed, can enter into the development and application of environmental law.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.442

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 studies0.0010.001
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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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