Intergenerational learning for active ageing: Chinese senior immigrants’ online learning during the COVID-19
Bibliographic record
Abstract
The COVID-19 has greatly affected the immigrant community in terms of racial and ethnic inequalities, border closures, travel restrictions, service processing delays, and/or mental health problems (Clark, et. al. 2020). During the pandemic, older immigrants experienced not only a high risk for severe illness, but also long-term and short-term uncertainties in both their families and the local society. The older immigrants could not receive adequate health support and social services, and lifelong learning opportunities. The purpose of this paper is to explore the relationship between intergenerational learning and ageing in immigrant communities. Through an examination of how Chinese senior immigrants learn intergenerationally in Canada during the COVID-19, this paper argues that intergenerational learning could be seen as a pathway for active ageing (WHO, 2002), which helps to enhance senior immigrants’ health and wellbeing, civic engagement, and social security. Based on a self-initiated intergenerational learning project by a Chinese senior immigrants’ association in Toronto, Canada, this study examined how older immigrants practiced intergenerational learning with immigrant youth through a variety of online teaching and learning activities, including English language learning, arts learning and other online collaborative learning activities. The study interviews 16 Chinese senior immigrants and 10 immigrant youth participating in the intergenerational activities in Toronto. Results showed that online intergenerational learning served as a lifelong learning process for active ageing, and played important roles in 1) intergenerational knowledge transfer; 2) cross-cultural communications; and 3) online civic engagement and community development.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".