Rewilding Play: Design Build Interventions
Bibliographic record
Abstract
Research on physical interventions installed in outdoor environments and their impacts on children’s play and development is a growing area of study. This paper focuses on the design and installation of outdoor interventions at early childhood education centres in Vancouver, Canada and the impact that theses interventions had on play affordances. With the aim of intervening with inexpensive natural materials and loose parts, graduate students designed, built, and installed interventions and using the Seven Cs evaluation form they scored the play spaces pre- and post-installation. Design methods included the Seven Cs design guidelines and the Two-Eyed Seeing model. Students also sought the insights of Early Childhood Educators, maintenance staff, licensing officers, the British Columbia Cancer Agency, and an Indigenous herbalist/educator. They also examined and addressed solar modifications to create dappled light. To understand the impacts of the student interventions researchers compared the pre- and post-intervention Seven Cs scores, which increased by 20 to 30 points. Researchers seeking to replicate this type of project in their own institutions should carefully consider the impact of climate change on construction timing and material selection, and sensitivity to the diversity of socio-cultural values embedded in the community and within design decisions and the interventions themselves.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".