Beyond the fence: Exploring forest preschool/school approaches in Australia
Bibliographic record
Abstract
The origins of forest preschool/school approaches are attributed to Scandinavian countries, where children can frequently be found playing outdoors in all weathers as an integral aspect of their education programs (Knight, 2013a; Willia1ns-Siegfredsen, 2012). Such approaches are linked intrinsically to Scandinavian culture and landscape, specifically in Denmark, where WilJiams-Siegfredsen (2012, p. 7) describes frilufts/iv, or the 'free air life', as a long-standing cultural tenet. While forest preschools for young children up to the school-entry age of 6 years have been common in Scandinavia for decades, only in the 1990s was the forest preschool approach introduced into the United Kingdom. Since the 1990s, there has been an exponential growth in this approach across both preschools and schools, and internationally in many countries, including Australia, Ca11ada, Japan, New Zealand and the United States (Knight, 2013b). In Canada and the United Kingdom, this growth has recently led to over-arching professional associations, the Canadian Forest School Association and the United Kingdom Forest School Association, which offer guiding principles, practical information, publications, research and collaborative potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".