Lifelong Learning and Community Empowerment Among Older Chinese Immigrants in Canada
Bibliographic record
Abstract
Abstract Lifelong learning is essential for encouraging social inclusion, resilience, and empowerment among older adults, especially in immigrant communities. The Chinese Age-well Research and Education (CARE) initiative tackles key challenges faced by older Chinese immigrants in Canada through several projects. The Intergenerational Dialogue on Elder Abuse project builds awareness, provides emotional support, and strengthens ties between generations to prevent older adult’s mistreatment. The Intergenerational and Intercultural Dialogue to Fight Racism project teaches older Chinese immigrants about systemic racism, how it connects with ageism, and ways to respond to discrimination. CARE also supports digital literacy and storytelling, giving older adults tools to share their stories and connect with wider social issues. These efforts show how working across generations and offering focused education help older Chinese immigrants address social challenges, create strong support networks, and take part in community advocacy. Many older immigrants deal with language difficulties, cultural gaps, and struggles to recognize or handle discrimination, making accessible and culturally sensitive learning vital. Through research, education, and community collaboration, CARE uses lifelong learning as a means of social change, emphasizing the need for inclusive policies and programs to support the well-being and active involvement of older immigrant.
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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 | 0.001 | 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 | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".