Bibliographic record
Abstract
There can be no doubt that aesthetic appreciation of nature has frequently been a major factor in how we regard and treat the natural environment. In his historical study of American environmental attitudes, environmental philosopher Eugene Hargrove documents the ways in which aesthetic value was extremely influential concerning the preservation of some of North America's most magnificent natural environments. Other environmental philosophers agree. J. Baird Callicott claims that historically ‘aesthetic evaluation… has made a terrific difference to American conservation policy and management’, pointing out that one of ‘the main reasons that we have set aside certain natural areas as national, state, and county parks is because they are considered beautiful’, and arguing that many ‘more of our conservation and management decisions have been motivated by aesthetic rather than ethical values’. Likewise environmental philosopher Ned Hettinger concludes his investigation of the significance of aesthetic appreciation for the ‘protection of the environment’ by affirming that ‘environmental ethics would benefit from taking environmental aesthetics more seriously’. Callicott sums up the situation as follows: ‘What kinds of country we consider to be exceptionally beautiful makes a huge difference when we come to decide which places to save, which to restore or enhance, and which to allocate to other uses’ concluding that ‘a sound natural aesthetics is crucial to sound conservation policy and land management’.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".