A Decision Support Toolkit for the Design of Children-Oriented Urban Outdoor Learning Environments
Bibliographic record
Abstract
Urban rapid development has created poor open spaces and fewer outdoor learning environments for children. This issue highlights the quality design of urban public and open spaces that can significantly help children’s healthy growth and development. The urban plaza is one of the potential urban open spaces that positively affect the development of talent and creativity in children and develop their physical, social, emotional, and cognitive skills. As public art is an encouraging landscape medium, integrating public art in urban plazas can aid substantially in achieving this goal. The lack of a decision support tool for designing inclusive learning environments at urban plazas has persuaded us to develop the children-oriented urban plaza design (COUP) Toolkit. The COUP Toolkit is a universal tool that can aid design professionals and children’s therapists in assessing and evaluating the impact of urban plaza design on children’s skills growth. By applying the analytical hierarchy process (AHP) method, it was found that the Urban Oasis type of urban plaza contributes the most to skills development in children (WAlt.3 = 0.336), compared to other types (i.e., street plaza, corporate foyer, transit foyer, street, and grand public place). The COUP toolkit has 14 features for designing the urban plazas embedding the public art. Applying AHP indicated that performance art (W2.2 = 0.106), interactive instrument art (W2.3 = 0.106), and spatial composition (W1.1 = 0.103) are most vital in the design of a children-oriented urban plaza. The COUP toolkit was validated through a case study, the Musical Swings (i.e., 21 Balancoires) urban plaza in Montreal, Canada. The COUP toolkit is a rating system and proposes four grading labels based on the scores earned during the assessment process: gold, silver, bronze, and not-certified. The COUP toolkit determined that Musical Swings is ranked as silver; hence, it needs minor improvements, particularly in providing fountains (WSMC2.5 = 0.57) and wading pools (WSMC2.6 = 0.60) to create a proper learning environment for 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".