BUILDING MORE AGE-INCLUSIVE CAMPUSES BY ENGAGING WITH RETIRED FACULTY
Bibliographic record
Abstract
Abstract Identified in the Age-Friendly University (AFU) principles and in the Age Inclusivity Domains in Higher Education model, initiatives that support the campus retired and emeriti community are important elements to promoting age inclusivity and intergenerational connection. Such initiatives may be found in a myriad of forms and may include campus-specific efforts as well as organizational efforts. This symposium features campus leaders representing institutional partners of the Age-Friendly University global network who will discuss their recent efforts to understand the needs of and to engage with retired and emeriti faculty. June, Andreoletti, and Swanson (Central Connecticut State University) describe findings from an initial survey of retired and emeriti faculty that sought to understand their desire to remain engaged in intergenerational teaching, research, and service at the university. Similarly, Porter (University of Manitoba) and colleagues conducted an exploratory study at their university regarding retired academics who have specific positions as Senior Scholars and Professors Emeriti, with the goal to enhance their experiences and overall benefits for the university. Gautam (UMass Lowell) discuss their campus journey in engaging emeriti professors over the past several years and present student feedback from an “intergenerational team talk” activity which engages a retired professor and students in a Gerontology course. Finally, Montepare (Lasell University) reports on data collected from the American Psychological Association’s late career stage/retired faculty and addresses how professional organizations could support age-inclusive campus practices with more age-inclusive member practices. Cicero (University of Southern California) will share insights and pose future directions as the discussant.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".