Green outdoor environments: Settings for promoting children's health and wellbeing
Bibliographic record
Abstract
Around the world, children's outdoor environments, such as school grounds, early childhood education services, public playgrounds and backyards, are changing. Homogenous environments consisting primarily of asphalt and grass that are noted for being hot, hard and barren are being transformed or 'greened' into places designed to include a variety of natural elements, such as vegetable gardens, wetlands, trees, frog ponds, murals and butterfly gardens.Internationally, a number of not-for-profit organisations support the process of greening children's outdoor environments. Organisations and programs such as Evergreen in Canada, the Centre for Ecoliteracy in the United States, Learnscapes in Australia, Movium in Sweden, the Ecoschools programs in South Africa and Learning Through Landscapes in the UnitedKingdom continue to grow in their profile and scope. These organisations provide guidance, funding and resources to administrators, teachers and parents who are interested in beginning the process of playground greening.Evidence of the wide-ranging benefits of green outdoor environments for children is mounting. These benefits extend to children's environmental awareness; opportunities for learning and cognition; social, emotional and mental wellbeing; and safety and health (Dyment, 2005; Gill, 2014; Rosenow & Bailie, 2014). Green outdoor environments can thus be considered and developed as sites to enhance these various dimensions of children's lives. They also represent an opportunity for educators working in schools and early childhood education services to reach out and collaborate with people working in other related educational fields and movements-be it around issues of achievement, citizenship, peace, sustainability, safety or health.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".