INTEREST GROUP SESSION—AGE-FRIENDLY UNIVERSITY (AFU): AGE-FRIENDLY UNIVERSITY CAMPUSES: PUTTING PRINCIPLES INTO PRACTICE
Bibliographic record
Abstract
Abstract The pioneering Age-Friendly University (AFU) initiative, endorsed in 2016 by GSA’s Academy for Gerontology in Higher Education (AGHE), calls for institutions of higher education to respond to shifting demographics and the needs of our aging populations through more age-friendly programs, practices, and partnerships. Over 45 institutions in the United States, Canada, European countries, and beyond have joined the network and adopted the 10 AFU guiding principles. This symposium will feature leaders at AFU campuses who will discuss why their institution joined the initiative, their age-friendly campus vision, and how they are putting AFU principles into practice. AFU Washington University St. Louis leaders will describe efforts to increase age-diversity on their campus, especially through professional studies programs that support personal and career development in the second half of life. AFU University of Southern California leaders will discuss new age-friendly efforts entailing intergenerational exchange, best practices of age-friendly programming for retired employees and alumni, and emerging connections to local community aging initiatives. AFU Eastern Michigan University leaders will round out the presentation with an overview of accomplishments of their campus-wide steering committee and its age-friendly evaluation research that informed the needs and interests of older learners pursuing second careers, as well as how to actively engage emeritus faculty and staff as a retired community on campus. The discussant will provide integrating comments and age-friendly suggestions for putting AFU principles into practice.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.081 | 0.037 |
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".