Bibliographic record
Abstract
This chapter delves into the intricate dynamics of designing environments tailored to meet the needs of aging populations, focusing on the challenges and strategies involved in accommodating the growing elderly demographic. With global projections indicating a significant surge in the senior population, reaching nearly 2.1 billion by 2050, the urgency to address their evolving needs is paramount. This chapter underscores the importance of fostering environments conducive to “aging in place,” allowing seniors to maintain autonomy and dignity within their own homes while alleviating the strain on healthcare systems. Drawing from research and case studies, this chapter highlights key considerations in outdoor urban design, emphasizing the significance of safe and accessible built environments to promote physical activity and mitigate the risk of falls, which remain a major concern for older adults. Furthermore, it explores the cultural and societal aspects of elderly care in diverse contexts, exemplified by observations from Bari, Italy, and the design initiatives in Middlesex, Ontario. In Bari, intergenerational cohabitation and communal activities underscore a cultural ethos that prioritizes the wellbeing and integration of the elderly, offering insights into fostering supportive communities. Meanwhile, the design approach in Middlesex reflects a proactive stance toward addressing the housing and care needs of aging populations, incorporating principles of universal design and multi-generational living arrangements. Overall, this chapter advocates for collaborative efforts across sectors to develop innovative solutions that cater to the diverse needs of aging populations, emphasizing the importance of inclusive design, community engagement, and cultural sensitivity in creating environments that promote active aging and social inclusion.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".