Bibliographic record
Abstract
What does an age-friendly city look like? ‘Age-friendly’ cities aspire to be places where people of all ages feel involved, valued, and supported with infrastructure and services that meet their preferences, needs and aspirations (World Health Organization). However, age-friendly will mean different things to different people, and it will require different approaches and priorities in different cities. The Ageing in Place research project is exploring how cities can meet the needs of the diverse range of older people to become more age-friendly. The research is drawing on insights from Akita, Bilbao, Brno, Brussels, Manchester, Oslo and Québec. The project relies on collaborations with many stakeholders across the seven cities, these include officers from local councils, community and private sector organisations, researchers from a range of institutions and older residents in the cities. As part of the Ageing in Place project, we asked older people what ‘age-friendly’ meant to them. This film shares some of what they said. The older people in the film are residents of their cities, volunteers at community centres, members of older people boards and recipients of age-friendly services. Their views and opinions represent a diversity of experience and involvement in age-friendly cities and communities. We hear in the film about transitions into retirement and older age, the desire to be useful and take part in activities, and the need for services that meet their changing needs. We also hear about changing urban environments and what this means for people ageing in cities. Across all the cities older people have stories to tell and experiences to share. The key message the film shares is that to make cities great places to age, the voices of older people must be central.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".