Age-Friendly Universities: Confronting Ageism and Fostering Age Inclusivity
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 aging populations through more age-friendly programs, practices, and partnerships. Over 65 institutions in the United States, Canada, European countries, and beyond have joined the network and adopted the 10 AFU principles. Despite the importance and appeal of the AFU initiative, individuals leading age-friendly efforts on their campuses have found that ageism in higher education is a persistent, yet overlooked, factor holding us back from embracing age diversity. This symposium will feature AFU partners who will discuss how ageism presents itself in higher education, along with offering recommendations for breaking it down and promoting greater age inclusivity. Montepare will open the session with an overview of systematic and implicit instances of ageism in higher education. Bowen and colleagues will then discuss results from an AFU Campus Climate Survey that examined the age attitudes of faculty, students, and staff along with their views about that nature of campus age-friendliness. Manoogian will discuss the value of approaching the teaching of age diversity from an intersectionality perspective. Reynolds and Kruger will provide theoretical framing and dissemination models for the GSA online course Ageism First Aid within various AFU and programmatic structures. Andreoletti and June will discuss how creating an age-inclusive AFU Learning Community can raise awareness about ageism across campus as well as in the community where a campus resides. Age-Friendly University (AFU) Interest Group Sponsored Symposium.
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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.032 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".