Bibliographic record
Abstract
This book provides K–8 educators with practical, research-based guidance for implementing—and advocating for!—risky play at school. Children are naturally drawn to risky play—taking physical chances, seeking excitement, and satisfying curiosity—and are more physically active when playing outdoors. Embracing Risky Play at School introduces readers to outdoor play and learning (OPAL) in the kindergarten to middle school years, explains the difference between risks and hazards , presents the concept of “Yes” spaces, and describes how and why risky play supports academic development and student well-being . Zeni and Brussoni, two experts in the field with decades of experience, share research evidence alongside accessible strategies for overcoming common barriers to implementing risky play at school. The Canadian Pediatric Society recommends risky play as a preventative measure for childhood obesity, anxiety, and behavioral issues , and schools have an important role to play in providing access to the unique benefits of risky outdoor play. This book addresses the collective responsibility of adults to support risky play when designing learning environments. Book Features: A clear understanding of what risky play is, and is not, particularly when supervising other people’s children as a professional educator in group learning environments. Guidance for creating conditions and building capacity for risky play in schools, which serves to support the physical, social, emotional, and cognitive growth of children. Links to learning modules that extend chapter content through the authors’ Outside Play website (www.outsideplay.org/).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".