Quality of urban life among older adults in the world major metropolises: a cross-cultural comparative study
Bibliographic record
Abstract
ABSTRACT The concept of quality of urban life (QoUL) can be interpreted quite differently across different cultures. Little evidence has shown that the measure of QoUL, which is based on Western culture, can be applied to populations cross-culturally. In the current study, we use data from the 2006 Assessing Happiness and Competitiveness of World Major Metropolises study to identify underlying factors associated with QoUL as well as assess the consistency of the QoUL measurement among adults, aged 60 and older, in ten world major metropolises (i.e. New York City, Toronto, London, Paris, Milan, Berlin, Stockholm, Beijing, Tokyo and Seoul). Exploratory factor analysis and multiple-group confirmatory factor analysis (CFA) are used to analyse the data. Findings of the study suggest that the measure of QoUL is sensitive to socio-cultural differences. Community factor and intrapersonal factor are two underlying structures that are related to QoUL among older adults in ten metropolises cross-culturally. Results from the CFA indicate that Toronto is comparable with Beijing, New York City, Paris, Milan and Stockholm in QoUL, while other cities are not. The results provide insights into the development of current urban policy and promotion of quality of life among older residents in major metropolitan areas. Future researchers should continue to explore the relationship between QoUL and socio-cultural differences within international urban settings, while remaining cautious when making cross-cultural comparisons.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".