Listening to children in nature: emergent curriculum in science teaching and learning in bush kinders
Bibliographic record
Abstract
Outdoor early childhood education contexts such as nature kindergartens have been established for well over 50 years in the United Kingdom and Scandinavia. Despite their popularity in these settings, similar approaches in countries such as Canada, China, New Zealand and Australia have taken time to become established. One example of where this approach to nature kindergartens in early childhood education has recently taken a foothold is the Australian ‘bush kinder’. Bush kinders are a context where four- to five-year-old preschool children can experience and learn biological, chemical and physical sciences in natural environments through play. This paper draws on research undertaken in bush kinders, applying ethnographic data. Data collection commenced in 2015 and subsequently, members of the research team returned to bush kinders in 2017, 2020 and 2023 to understand how the bush kinder approach to curriculum has continued to develop and grow. We respond to two research questions in this paper, (1) What are teachers’ experiences of an emergent science curriculum in bush kinder setting? And (2) How does an emergent science curriculum develop within bush kinder settings? Through observing educators’ application of emergent curriculum, we found that time spent in bush kinder provides children with the voice to articulate their science understandings.
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 | 0.002 | 0.003 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".