Leading Age-Friendly University Initiatives at a Research Intensive University in Manitoba, Canada
Bibliographic record
Abstract
Abstract When Dublin City University introduced the Age-Friendly University (AFU) principles in 2012, the Centre on Aging at the University of Manitoba had been fully immersed in age-friendly cities research, so it was a natural for it to champion the University becoming more age-friendly. As a university-wide research centre, at a research intensive university, the Centre on Aging has built on its strengths to advance the AFU movement. Related to its goals on research, knowledge mobilization, training, and partnerships, it has been able to work with its many Research Affiliates, students and community/university partners to tackle new AFU initiatives. Recent activities have included: an awareness raising workshop/showcase, discussions with Research Affiliates about potential new degree course offerings, an environmental scan of post-secondary inter-generational possibilities, a home-sharing project, workshop development on older learners in the classroom, and provincial community workshops and consultations on communicating about aging and healthy aging. Part of a symposium sponsored by Directors of Aging Centers Interest Group.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".