The impact of playground design on play choices and behaviors of pre-school children
Bibliographic record
Abstract
The purpose of this study was to examine where and how children choose to play in four Australian pre-school centers with very different outdoor playgrounds. Using a momentary time sampling direct observation instrument, a total of 960 scans were taken of pre-determined target areas (paths, paved expanses, grass, softfall, sand feature, manufactured functional, manufactured constructive and natural) within four playgrounds over a 30-day period. During each scan, we recorded the number of boys and girls observed in each target area as well as the dominant type of play (functional, constructive, symbolic, self-focused, talking). A total of 2361 observations of children occurred across the four centers. The results revealed the children were using the four playgrounds differently. At the diverse and natural Center A, the most popular space was the natural area and the least popular space was the sandpit. At the small, compact and diverse Center B, children were fairly evenly dispersed, with the most popular areas being the softfall and paved expanse. At the hard and barren Center C, almost half the children were found on the pavement, but the sandpits and natural areas were also popular. Finally, at the large, sparse and old Center D, children were fairly evenly dispersed, but most were observed playing on the softfall. Across all centers, irrespective of target area, the dominant play activity was functional play followed by self-focused play. This article discusses these findings and asks important questions about the design of pre-school playgrounds. In doing so, this study has begun to explain the relationship between the design of outdoor play spaces, children's choices of play locations and their play behaviors.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".