THE IMPACT OF CULTURE ON CIVIC ENGAGEMENT OF AGING ASIAN IMMIGRANTS: FINDINGS FROM A MIXED STUDY IN EDMONTON, CANADA
Bibliographic record
Abstract
Abstract As a well-known immigrant-receiving country, Asian immigrants constitute most immigrants in Canada. However, the understanding of cultural diversity and intra-cultural similarities and differences among aging immigrants’ civic activities is limited. A mixed-methods study was conducted to examine civic participation experienced by Filipinos, Indians, and Chinese, since they are the three largest ethnocultural communities in Edmonton. Thematic analysis related to research questions was used for data analysis. Findings show diversity in the understanding of civic engagement and willingness, and their understanding and engagement in civic activities are affected by culture-related factors such as country of origin, time since immigration, citizenship status, greater official language proficiency, and pre-migration participation in activities. Public policies, such as immigration and integration policies (e.g., multiculturalism, diversity, and social inclusion), also influence immigrants’ civic participation. The findings suggest that cultural diversity should be considered in promoting civic activities among aging immigrants.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".