AGE INCLUSIVITY DOMAINS OF HIGHER EDUCATION (AIDHE): A MODEL TO SUPPORT AGE-DIVERSE STUDENTS, FACULTY, AND STAFF
Bibliographic record
Abstract
Abstract The Age Inclusivity Domains of Higher Education (AIDHE) model (adopted in 2023 by GSA and its Academy for Gerontology in Higher Education) offers a guiding framework for how institutions of higher education can assess age-inclusive practices that impact students, faculty, and staff across seven domains of institutional function. In this symposium, presenters describe campus practices in three high-need, high impact domains: Teaching and Learning, Personnel, and Student Affairs. To begin, Bowen and colleagues (UMass Boston/Lasell University) will describe the AIDHE model and research using the Age-Friendly Inventory and Campus Climate Survey (ICCS) which identified challenges within each domain along with evidence-based transformative and actionable strategic approaches. Reflecting the domain of Teaching and Learning, Porter (University of Manitoba) will describe a new micro-certificate program, Facilitating Older Adult Learning, that provides educators with pedagogical design tools to select and evaluate appropriate learning strategies for supporting effective older adult learning. Reflecting the domain of Student Affairs, Galucia and Morrow-Howell (Washington University in St. Louis) then will describe their research with admissions, career services, and DEI staff about how to best support non-traditional aged students. They will also discuss Next Move, a program for graduate students returning to school after substantial work and life experiences. Reflecting the domain of Personnel, Kaskie (University of Iowa) will discuss the aging professorate and how institutions can modify human resource policies and programs to provide more opportunities for aging faculty and staff to remain healthy and productive. Halvorsen (Boston College) will serve as discussant. Age Inclusivity in Higher Education 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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".