UNDERSTANDING INTERGENERATIONALITY: THEORIES, REFLECTIONS, AND EXPERIENCES
Bibliographic record
Abstract
Abstract Research into different aspects of intergenerationalities continues to develop at a considerable pace for individuals, communities, and society more generally. A number of programs and practices for older people are organized around the presumed benefits of intergenerational interaction between younger and older people, with intergenerational programming operating as a taken-for-granted practice. However, the merits of this approach, the models that inform practice, and the learning that takes place between older and younger people, remain under-theorized. This poster reviews and discusses dominant theoretical frameworks including reflections and experiences from intergenerational learning programs in Canada (e.g. Co-Housing). It documents how the field of intergenerationality is conceptualized and executed in the realms of theory and practice; how models retain age and stage-based assumptions, including the polarizing discourses of ‘decline’ and ‘activity’; and discusses the implications for methodology, application, and outcome measures. By understanding the underlying assumptions utilized in the field of intergenerational learning, this poster makes an important contribution to the theoretical foundations, methods, and approaches, that are required to build more appropriate intergenerational landscapes.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.050 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".