CREATING OPPORTUNITIES TO IMPROVE AGE INCLUSIVITY IN HIGHER EDUCATION: AN INNOVATIVE EDUCATION PROGRAM EXAMPLE
Bibliographic record
Abstract
Abstract The Centre on Aging at the University of Manitoba (UM) leads the UM Age-friendly University (AFU) Committee and has taken on many initiatives to enhance the age inclusivity of the University, since 2016. In 2021, the Centre held a competition called the Age-friendly University Initiative Fund, which was open to all academic and non-academic units at UM. Funded projects related to campus wayfinding (Architectural & Engineering Services and the Office of Sustainability), inter-generational arts presentations/workshops (School of Art Gallery), technology training (Alumni Relations), and the development of a micro-certificate on Facilitating Older Adult Learning (FOAL; Faculty of Extended Education). This presentation will provide an overview of the Age-friendly University Initiative Fund and its collaborative process of working with funded projects, as well as provide specific details on the development and implementation of the micro-certificate. The FOAL micro-certificate provides a professional development opportunity to take three modules online: older adult development, universal design for learning, and using technology for teaching and learning with older adults. In total there are 36 contact hours for this credential that can be completed in a flexible fashion. The goal of the micro-certificate is to enable individuals to work more effectively with older adults to support their learning, across a broad range of learning environments (e.g., lifelong learning programs, senior centers, health care and therapy settings). Cross-unit collaboration is critical for the effective development and implementation of initiatives to improve age inclusivity across multiple domains such as those described in this presentation.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".