AGING IN PLACE IN URBAN SETTINGS: HOW TO BETTER UNDERSTAND CURRENT AND FUTURE LINKS BETWEEN PERSON AND ENVIRONMENT
Bibliographic record
Abstract
Environmental Gerontology addresses concepts and empirical evidence on person-environment exchange processes, related predictors and developmental outcomes in later life. The aim of this symposium is to focus on the typical example of aging in place in urban settings and highlight factors that facilitate ‘aging in the right place’, as well as challenges that contribute to older adults being ‘stuck in place’. The first paper explores the role of mobility and other socio-spatial factors for aging in place in innovative housing options and neighborhood arrangements in cohousing and naturally occurring retirement communities (NORCS). The second paper focuses on the role of mobility-related behavioral flexibility and routines in out-of-home mobility of older adults mainly from a psychological perspective. The third paper links health and environment by highlighting the importance of environment and the personal biography in understanding health literacy in later life. The fourth paper shifts the focus to more macro environmental issues of climate change and urban ecology, and highlights how these macro environmental factors affect the person and environment exchanges for older adults now, and how this may evolve in the future for aging in place. Finally, the discussion provides an outlook on future urban life, the challenges and opportunities for aging in place, and the implications for research, practice and application.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.029 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".