Bibliographic record
Abstract
UN reports generally concentrate upon quantitative analysis of the direct drivers of ecology on global poverty and ecosystem change, but the contributors to the Millennial Ecosystem Assessment (MA) have initiated a discussion of 'indirect driv- ers' - the relation of culture, aesthetics and spirituality to global climate change - and, for the first time, have made this qualitative evidence endogenous to their models. The MA validates ecological aesthetics as a science of quality but finds difficulty in present- ing evidence in support of its claim. Ecological aesthetics has achieved prominence at local level as well, among those in forestry management of national, provincial and state parks in the United States and Canada. Yet they too find difficulty in assessing evidence; indeed their attempts to derive a match between perceptual categories of aesthetic beauty and ecological sustainability have generally failed. The qualitative sci- ence of ecological aesthetics which Bateson developed towards the end of his life offers several avenues out of the near impasse in these two cases. Bateson studies ecological aesthetics at a second order level, stressing the contextual difference between industrial society's understanding of basic categories of space, time and connectivity, and the same categories perceived from a more 'holistic' point of view - ecological aesthetics as a form of conservation of time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".