Bibliographic record
Abstract
Population ageing and urbanization are two global trends that together comprise major forces shaping the 21st century. At the same time as cities are growing, their share of residents aged 60 years and more is increasing. Older people are a resource for their families, communities and economies in supportive and enabling living environments. WHO regards active ageing as a life-long process shaped by several factors that, alone and acting together, favour health, participation and security in older adult life. Informed by WHO’s approach to active ageing, the purpose of this Guide is to engage cities to become more age-friendly so as to tap the potential that older people represent for humanity. An age-friendly city encourages active ageing by optimizing opportunities for health, participation and security in order to enhance quality of life as people age. In practical terms, an age-friendly city adapts its structures and services to be accessible to and inclusive of older people with varying needs and capacities. By working ing with groups in 33 cities in all WHO regions, WHO has asked older people in focus groups to describe the advantages and barriers they experience in eight areas of city living in: Amman, Jordan Cancun, Mexico Dundalk, Ireland Geneva, Switzerland Halifax, Canada Himeji, Japan Islamabad, Pakistan Istanbul, Turkey Kingston and Montego Bay (combined), Jamaica La Plata, Argentina London, United Kingdom Mayaguez, Puerto Rico Melbourne, Australia Melville, Australia Mexico City, Mexico Moscow, Russian Federation Nairobi, Kenya New Delhi, India Ponce, Puerto Rico Portage la Prairie, Canada Portland, Oregon, United States of America Rio de Janeiro, Brazil Ruhr metropolitan region, Germany Saanich, Canada San Jose, Costa Rica Shanghai, China Sherbrooke, Canada Tokyo, Japan Tripoli, Lebanon Tuymazy, Russian Federation Udaipur, India Udine, Italy
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".