‘Don’t Pick, Don’t Lick’: Connecting Young Children’s Risky Play in Nature to Science Education in Australian Bush Kinders
Bibliographic record
Abstract
Abstract The forest school approach to nature learning has gathered momentum in the UK and across parts of Europe including Scandinavia for well over 50 years. In other contexts that include Canada, China, New Zealand and Australia, nature-based early childhood education and care settings, influenced by European forest school approaches, are in a rapid expansion phase as educators and policy makers acknowledge the benefits for children from time spent in nature. It is known that science education opportunities exist in these nature-based settings, yet this area has only garnered limited research attention to date. An example of a nature-based approach to early childhood education which emerged in the 2010s is the Australian ‘bush kinder’. Four- to five-year-old preschool children experience and learn from nature through play in bush kinders. This paper highlights the intersections that occur between risky play and science teaching and learning in the context of bush kinders. Through analysing research in early years science education, guiding curriculum frameworks and early childhood learning, I propose the importance of children’s risky play to early childhood science education. Drawing on vignettes from ethnographic fieldwork data, the merits of risky play in bush kinders to embed science knowledge is illustrated here. Participant observation was used to build a profile of each site and to gather data relating to how educators draw on children’s risky play to seek out opportunities to teach children about physical, chemical and biological science.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".