Bibliographic record
Abstract
In Canada, as in many countries around the world, the proportion of older adults is increasing. In addition, more people of all ages are living in cities. The Age-Friendly Cities concept was spearheaded by the World Health Organization (WHO) in response to these two major world-wide trends of global aging and urbanization. The underlying premise of age-friendly cities is that they foster the health, security, and social participation of older adults, or what the WHO refers to as 'active aging.' In 2007, the WHO launched the "Global Age-Friendly Cities: A guide" which identified core age-friendly city features. As a follow-up, Canada launched the "Age-Friendly Rural and Remote Communities: A guide" which provided a framework for understanding some of the issues faced bv the smaller communities characteristic of Canada and other parts of the world. Along with several other provinces within Canada, Manitoba has embraced the concept of creating age-friendly communities and is recognized as a global leader in the initiative. This presentation will provide a background on the Age-Friendly Cities initiative and on creating age-friendly environments. Particular attention will be paid to the process of creating age-friendly communities in Manitoba and possible ways that the physical and social environment can intersect with issues of hearing, communication, and social participation of older adults.
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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.026 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".