Urbanization and Ageing: Ageism, Inequality, and the Future of “Age-Friendly” Cities
Bibliographic record
Abstract
Two major forces are set to shape the quality of daily life in the twenty- first century: population ageing and urbanization. Both have become major concerns for public policy, with significant implications for all types of communities. Cities are now regarded as central to economic development, attracting waves of migrants and supporting new knowledge-based industries. However, the extent to which the new “urban age” will produce what the World Health Organization have termed “age-friendly” cities and communities, creating opportunities for older people as well as strengthening ties across different age and social groups, remains uncertain. This article examines the relationship between ageing and urbanization through the application of the concept of ageism. It argues that urban development, especially that operating over the course of the 2000s and 2010s, has both consolidated and introduced new inequalities in the lives of older people. This is examined in three main ways: first, in the context of research on urbanization and the field of urban sociology in particular; second, through examining a range of examples where ageism may be said to operate within the urban environment; and third, outlining the basis for promoting an “anti-ageist urbanism” focused upon challenging inequality and spatial injustice.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".