Green School Grounds as Sites for Outdoor Learning: Barriers and Opportunities
Bibliographic record
Abstract
In their review of evidence-based research entitled A Review of Research on Outdoor Learning, Rickinson et al. (2004) identify five key constraints that limit the amount of outdoor learning. This paper explores whether green school grounds might be a location where these constraints could be minimised. Specifically, it reports on a study that sought to investigate the use of green school grounds as sites for outdoor learning, to identify barriers that impede such use, and to examine how these barriers differ from those cited in Rickinson et al.’s review. A mixed method approach was used: (1) 149 questionnaires were completed by administrators, teachers, and parents associated with 45 school ground greening initiatives in a Canadian school board; (2) 21 follow-up interviews were completed at five of the schools. Study participants reported that green school grounds are used regularly for teaching some subjects, notably science and physical education, but considerably less for teaching language arts, mathematics, and geography. They also identified a series of barriers that limit the amount of outdoor learning and these are compared to those identified by Rickinson et al. (2004). This paper concludes with a discussion of how the opportunities for teaching and learning on green school grounds can be more fully maximised.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".