It takes a village: rethinking urban spaces for children with ADHD
Bibliographic record
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that impacts individuals’ ability to focus, organize, and regulate impulses. In landscape architecture, accessible design can be applied to address ADHD, which can positively impact the urban environment and create a more inclusive space for all. The exploration of this intersection of ADHD and landscape architecture highlights both the challenges and potential strengths of individuals with ADHD. In this way, it can pinpoint the factors contributing to a practical urban space that assists children with ADHD in a neighbourhood. To understand how to design for children with ADHD, one must first understand what ADHD is and how it affects children in families and their communities. Doing so can help identify the necessity to address the symptoms of ADHD in the urban landscape. Secondly, a neighbourhood analysis is conducted to understand how to design for the neighbourhood of Elmwood in Winnipeg, Manitoba. Through the analysis of ADHD and Elmwood, a universal design can address and respond to the symptoms of ADHD in children to enhance the overall outdoor environment for all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".