Making communities age friendly: state and municipal initiatives in Canada and other countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To promote healthy, active aging, the age-friendly community initiative has evolved in Canada, Spain, Brazil and Australia, among other countries. An age-friendly community provides accessible and inclusive built and social environments where older adults can enjoy good health, participate actively and live in security. The rapid expansion of the initiative in all states can largely be explained by common key activities undertaken by the state, municipal and -in the case of Canada- also federal, governments. These initiatives include strategic engagements and policy action in all states, and knowledge development and exchange in Canada in particular. Strategic engagements involve creating or strengthening collaborative intersectoral relationships to access multiple arenas of decision-making, and addressing all areas that constitute an age-friendly community. With variations across states, policy actions have included the following: declaring the initiative as an official policy direction; establishing model cities to be emulated by other cities; funding community projects; implementing consistent methodology; evaluating implementation, enhancing public visibility, and aligning age-friendly community policy with other state-level policy directions. To stimulate knowledge development and exchange, Canadian efforts have included the creation of a community of practice and of a research and policy network to encourage the development and translation of scientific evidence on aging-supportive communities. These activities are expected to result in a strong and durable integration of older persons' views, aspirations, rights and needs in municipal, as well as state, planning and policy.
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.
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) | 0.001 | 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 it