‘Emplaced Transcendence’ as Ecologising Education in Michael Bonnett's Environmental Philosophy
Bibliographic record
Abstract
At the 2019 Annual Conference of the Philosophy of Education Society of Great Britain, Michael Bonnett was an invited speaker, giving a paper entitled ‘Transcendent nature, sustainability, and ‘ecologising’ education’. In this keynote, Bonnett shared portions of his forthcoming book, Environmental Consciousness, Nature, and Philosophy of Education: Ecologizing Education (London, Routledge). A day earlier at the conference Jeff Stickney gave a paper entitled ‘Informal place-based learning in environmental sustainability education: Seeing anew the Cloister oak tree at New College through Heidegger & Wittgenstein's Philosophies.’ Here we offer a section of Stickney's paper discussing Bonnett's environmental philosophy, focusing on a central aspect of his work: aesthetic and place-based education as a way of connecting more deeply with ‘nature’. Instead of delivering his paper at the conference, Stickney led participants on a walk across the Oxford campus, talking about the place and seeking to enhance appreciation of the evergreen oak tree in the cloister by entertaining different philosophical perspectives while those gathered drew the tree. The exercise was meant to illustrate what aesthetic, emplaced transcendence might actually look and feel like, in practice. Bonnett humbly agreed to write a brief response for this Special Issue.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| 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".