An Inclusive Framework for Public Space: A Study of Design Strategies to Promote the Social Participation of People with Dementia
Bibliographic record
Abstract
China has the largest and fastest-growing population of people with dementia in the world. It has become urgent to create environments in urban areas where these populations can live and thrive. This paper provides an in-depth case study of dementia activity centres and dementia cafes in public spaces in China. It highlights contemporary social issues, such as the lack of universal education and dementia-friendly spaces. Through an examination of these challenges from the perspective of the built environment, this paper proposes cognitively friendly design strategies at the levels of appearance and function. It also incorporates principles of inclusive design, together with a theoretical framework for the construction of the construction of cognitively dementia-friendly spaces. Finally, this paper explores these strategies from the perspectives of spatial transformation, people's aspirations and social support.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".