Children’s wellness: outdoor learning during Covid-19 in Canada
Bibliographic record
Abstract
Outdoor learning has been a topic of recent discussion due to the Covid-19 global pandemic, which led to the closure of many schools, daycares, and regular programming, and the negative repercussions that affect children. Here, we consider the changing practices related to outdoor learning and Indigenous land-based learning during the pandemic, and the implications for children’s wellbeing, development and learning. Indigenous culture, relating to traditional learning and knowledge, and cultural connections to the land, is also considered to interrogate how outdoor, nature-based, and on the land experiences affect community wellness. This paper draws on interviews with the leaders of two forest schools, Cloudberry Forest School and ForestKids, and the creator of the 1000 Hours Outside Program. Common themes, such as ‘nature as the teacher’ (trust regulation), environmental stewardship, social cohesion, physical and mental well-being, and Indigenous’ ways of knowing were identified across the interviews. This study reveals practical implications for teachers regarding the importance of access to nature based free play children’s development, and the importance of land- based education for Indigenous children.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".