INTEGRATING AGE INCLUSIVITY WITH DEI EFFORTS ON AGE-FRIENDLY UNIVERSITY (AFU) CAMPUSES
Bibliographic record
Abstract
Abstract The pioneering Age-Friendly University (AFU) initiative, endorsed 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 age-diverse, older populations through more age-friendly programs, practices, and partnerships. Over 85 institutions in the United States, Canada, European countries, and beyond have joined the global network and endorsed the 10 AFU principles, with even more showing interest in becoming partners in the movement. One key foundational area identified by AFU research efforts and partners is integrating age inclusivity with ongoing diversity, equity, and inclusion (DEI) efforts on campuses. This symposium explores the need for this integration featuring AFU partners who will offer their observations and recommendations. Bowen and colleagues will open the session with data from their national study of age-friendliness in U.S. institutions to describe their insights regarding the state of age diversity on campuses and the experiences of students, faculty, and staff that call for greater age inclusivity. Morrow-Howell and colleagues will present data from interviews with DEI officers that identify institutional considerations for inclusion efforts. Andreoletti and colleagues will offer specific curricular and related strategies for connecting age-inclusivity efforts with DEI campus efforts. Gugliucci will discuss considerations regarding age-inclusive images and messages in health professions education including the inclusion of identifiable DEI objectives in syllabi. As discussant, GSA president Lichtenberg will comment on age-inclusivity efforts in higher education within GSA’s broader commitment to diversity, equity, and inclusion.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.002 | 0.004 |
| 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".