BUILDING BRIDGES ACROSS GENERATIONS: THE INTERGENERATIONAL CLASSROOM
Bibliographic record
Abstract
Abstract The issue 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 within post-secondary classrooms settings is a novel approach towards building interest in the field of aging. The University of Toronto (UofT) partnered with Christie Gardens, a retirement community and long-term care home, to launch an innovative experiential learning initiative: The Intergenerational Classroom. Half the learners were UofT undergraduate students (n=24); the other half were older adults residing at Christie Gardens (n=25). Through interactive seminar-style discussions, collaborative projects and mentorship, the course, which was held at Christie Gardens, provided a semester-long exploration on aging. To evaluate the program, pre/post-surveys were administered to all learners, and following the semester, students (n=6) and older adults (n=6) participated in focus group discussions. Outcomes of program success were identified across domains, including meaningful friendships and bonds created, increased awareness of aging issues, reduced ageist attitudes, and greater sense of community and civic engagement. Evaluations revealed that 92% had an excellent learning experience, 95% found the course intellectually stimulating, and that 100% would recommend the course to others. The Intergenerational Classroom explored aging from a viewpoint that considered the perspectives of both older and younger generations. Intergenerational approaches can help to create a brighter and more inclusive future for all generations, ensuring that individuals can grow up and grow older with dignity, rights, and opportunity.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".