Bibliographic record
Abstract
As countries around the world are rapidly aging, cities and towns are finding new ways to harness the unprecedented opportunity and challenge of this newfound human longevity. The World Health Organization’s (WHO) Age-Friendly Cities model is an international framework to help cities assess and improve features of their communities that promote participation, health, and quality of life for older adults of all abilities, including adults living with dementia. This session brings together five international scholars who are focused on building these age-friendly communities to share their learnings and experiences. They will present their work and spark discussion about the ways they have managed to build communities that are inclusive of all older adults—including those with physical disabilities and cognitive impairment. Presenters from the United Kingdom, Ireland, Canada, China, and the United States will provide overviews of their approaches to building age-friendly environments, highlighting challenges and successes in ensuring the inclusivity of these communities extends to persons with disabilities and those living with dementia. In addition to international perspectives, these presenters come from a range of disciplines including urban planning, architecture, demography, and disability studies. This session will also provide an opportunity for open dialogue among presenters and attendees to share the diverse and creative ways they have addressed age-friendliness in their countries and communities. We have much to learn from one another about creating communities that promote inclusion, health, and quality of life for aging across the lifespan.
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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.023 | 0.045 |
| Open science | 0.003 | 0.042 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".