Never Too “Old”: Insights From the Intergenerational Classroom
Bibliographic record
Abstract
Abstract The topic of aging demands greater attention in education systems, and intergenerational approaches help to combat ageism and improve the lives of older adults, now and in the future. Taking an intergenerational approach to contextualizing the experiences of older adults is a step towards addressing challenges commonly associated with aging. The University of Toronto (UofT) partnered with Christie Gardens, a retirement community, to launch an innovative experiential learning initiative: The Intergenerational Classroom. Half the students were UofT undergraduates; the other half were older adults residing at Christie Gardens. Through interactive seminar-style discussions, collaborative projects and mentorship, the course, which was held at Christie Gardens, and offered in the Fall 2023 and Fall 2024 semesters, provided a semester-long exploration on aging. This study explored the impacts of the Intergenerational Classroom from the perspectives of undergraduate students and older adults who had participated in the program. Guided by a phenomenological qualitative methodology, this study conducted in-person, semi-structured interviews with a sample of undergraduate students (n = 10) and older adults (n = 16). To enhance trustworthiness, two researchers independently analyzed transcript data to identify key transcript statements into themes. Outcomes of program success were identified across domains related to lasting friendships and bonds, increased awareness of aging issues, reduced ageist attitudes, and community and civic engagement. The taxonomy developed provides a comprehensive and conceptually organized range of successful outcomes to serve as infrastructure for the development of meaningful intergenerational programming outcome measures.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".