Developing and testing an audit tool for activity-friendly parks in dense urban areas of Asia
Bibliographic record
Abstract
Parks are important urban design settings to promote physical activity within urban areas. However, existing park audit tools often do not address the unique challenges of high-density areas, especially in Asian contexts. This study presents the development and testing of the audiT tool for Activity-friendly Parks in denSe urban areas (TAPS) that support park-related physical activity in highly dense urban settings. Created through a Delphi consultation process that incorporated expert consensus, TAPS focuses on five key domains: park surroundings and accessibility, activity areas, facilities and amenities, aesthetics, and safety. The tool was tested in 25 parks across Tokyo, Japan. Of the 24 park attributes identified by interdisciplinary experts, open/green spaces and pathways had the highest expert consensus. Inter-rater reliability was measured using Cohen’s kappa and percent agreement; validity was confirmed through comparison to a gold standard. Across the items, 91.1% achieved a kappa of over 0.4 indicating at least moderate agreement and 95.9% showed more than 70% agreement. The overall dimension validity displayed 87.5% agreement. TAPS is a user-friendly tool that provides a reliable and valid evaluation framework for improving parks to support physical activity in dense urban areas in Asia.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".