Unveiling voices and narratives: exploring the perspectives on multiculturalism and bilingualism of recent adult Chinese immigrants in Canada
Bibliographic record
Abstract
Multiculturalism, a federal policy that emphasises cultural, social, and political rights for ethnic minorities in Canada, is a crucial aspect of immigrant life. Concurrently with the implementation of multiculturalism by the federal government, Canada adopted bilingualism as its official language policy. Although the primary motivation for this policy was to protect the French language and culture, it holds significant relevance for immigrants, especially those residing in Francophone-dominant areas such as Quebec. Despite the importance of these policies to immigrant life, few studies have explored immigrants’ perspectives on both federal policies. Addressing this gap, the present study examines the lived experiences of six adult Chinese immigrants who arrived in Canada and obtained permanent residency or Canadian citizenship after 2010. Through narrative inquiry, this research shares participants’ stories to explore how recent adult Chinese immigrants understand Canadian multiculturalism and bilingualism. Taking a critical stance on multiculturalism and bilingualism, this study examines, discusses and interprets the lived experiences of its participants with the goal to confront issues of inequality, privilege, and power dynamics. The findings reveal that Chinese immigrants’ understanding of multiculturalism and bilingualism is shaped by their personal experiences with both policies. Participants leverage their experiential knowledge of multiculturalism and bilingualism to navigate their new lives in Canada. Although participants voice some concerns about these policies, they also presented nuanced perspectives and opinions on both federal policies. These perspectives not only enrich the literature on Canadian multiculturalism and bilingualism, but also highlight Chinese immigrants’ desire for integration in Canada.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".