Wildness, Cyborgs, and Our Ecological Future: Reassessing the Deep Ecology Movement
Bibliographic record
Abstract
George Sessions is a retired philosophy teacher who still offers courses now and then at Sierra College in Rocklin California. He is long associated with the deep ecology movement and has published many papers on ecologically related topics. He is the co-author of Deep Ecology and editor of Deep Ecology for the 21 Century. Many of the Naess papers cited in the notes are now available in the Selected Works of Arne Naess collection, especially volume 10 The Deep Ecology of Wisdom, which is devoted to Naess’s papers on the ecological crisis. For more information of the volumes, see not only this whole Trumpeter series 21.1–22.2 devoted to Naess but also visit www.springeronline.com. Sessions is a devoted wilderness journeyer.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".