Editorial for special issue: The serious side of nature, outdoor learning and play: international perspectives
Bibliographic record
Abstract
There is extensive outdoor learning research taking place across the world, which highlights the need to look beyond the dominant Eurocentric and UK-based perspectives. In this special issue we bring together leading authors from England, Ireland, Scotland, Australia, Canada and India to discuss ways of researching the health, wellbeing and educational benefits that may be provided throughout life within a range of outdoor learning contexts. Nature, outdoor learning and play is about more than fun and games – it also enables us to explore some of the most pressing problems facing the world, particularly mental wellbeing, climate change, biodiversity loss and finding positive ways for humans to more sustainably coexist with non-humans. Playful, nature-based activities provide ways of learning about the outside world and understanding our place within it, and enable the development of a more positive relationship with nature, other people and ourselves. This collection of papers makes a significant contribution to knowledge development and exchange from international perspectives, which is timely as the people of the world adjust to living with Covid-19, alongside ongoing, urgent environmental concerns. It is well documented that spending time outdoors is good for our health and wellbeing. However, access to outdoor spaces is inequitable and this has been exacerbated by public health responses to the pandemic.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".