Immigrants as active citizens: exploring the volunteering experience of Chinese immigrants in Vancouver
Bibliographic record
Abstract
Despite the fact that immigration has played an important role in transforming Canada into an ethno-culturally diverse and economically prosperous nation, immigrants themselves are often criticised as passive citizens. This study attempts to deconstruct this myth by investigating the volunteering experiences of Chinese immigrants in Vancouver. The study adopts a case study approach, drawing on questionnaires completed by 196 Chinese immigrants and additional personal interviews completed with 30 of these individuals in an immigrant service organisation in Vancouver. The findings show that volunteering is a powerful source of informal learning. Through volunteering, Chinese immigrants in this study learned language, skills and knowledge needed by new citizens for their integration into Canadian society. Volunteering also helped immigrants build a community and a sense of belonging. Furthermore, the study demonstrates that voluntary organisations can be important sites for immigrants to draw on in navigating complex paths to full citizenship and full participation in their new society. Findings of this study advance our extant knowledge of the participation of immigrants as active citizens, and how they fulfil their responsibilities as new citizens.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".