Bibliographic record
Abstract
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".