Bibliographic record
Abstract
This highly novel volume reframes the popular, yet sorely under-theorised international education movement known as forest school, offering an interdisciplinary framework with which to set apart forest school from other outdoor education programmes and child-led pedagogies. By broadly defining forest school as regular, repeated, unstructured practice in nature, the book is able to link the established UK-centred understanding of the practice to other similar international movements that share the same Scandinavian-inspired principles, such as those in Germany, the United States, Canada, South Korea, and New Zealand, among others. Centred around three central pillars which demonstrate how forest school may be viewed through three alternative onto-epistemological lenses, chapters engage with data by writing diffractively through these three concepts and with the key posthuman, eco-philosophical, and new materialist theories that relate to them. The book ultimately argues that it is the forest itself – and the quiet intra-relationship that develops – which is the integral keystone of the practice of forest school. This perspective challenges historical human-centred thinking, which has long constrained our understanding of the profound connections between humans and the natural world. Offering a new appreciation of the quiet power of forest school and an understanding of it as a significant emergent pedagogical practice, this book will be of interest to scholars, researchers, and postgraduate students involved with forest school, early childhood education, the philosophy of education, and theories of learning more broadly.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".