‘Mother‐trees’ and Teachers: Connecting My Daughter's Environmental Education with Diana Beresford‐Kroeger's Enduring Wisdom
Bibliographic record
Abstract
How does environmental knowledge move from an expert's books into the life and learning of a small community? Diana Beresford-Kroeger, a renowned author of The Global Forest and Arboretum America, A Philosophy of the Forest, kindly agreed to an interview with Simon Heath, organiser of a rural literary festival. In working with the transcripts, it dawned on Simon Heath (author) and Jeff Stickney (editor) that the educational story here is about community members gathering under a big tent to watch Diana's documentary, ‘The Call of the Forest’, hearing her eye-opening explanation of how trees can positively affect our health and climate. Inspired by seeing a small group of preteen girls fundraising and distributing maple saplings to the 150 people in attendance, the narrative follows the educational path of one of these children, attending an alternative Forest School that allows for discovery approaches to individualised environmental learning and activism. Phenomenologically, the vignette opens to view how words of wisdom, both scientific and spiritual, percolate into the lives of readers and listeners, inspiring both children and adults to begin changing how they live.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".