Becoming Canadian: Examining the Lived Experiences of Recent Adult Chinese Immigrants to Canada
Bibliographic record
Abstract
The 2021 Census reported that immigrants with a Chinese ethnic background take up 4.7% of the Canadian population. Although Chinese immigrants represent a significant part of the Canadian population, how they understand their relationship with Canada has not been studied directly in educational research. In response, this manuscript-based thesis examines the lived experiences of six adult (aged 18 and above) Chinese immigrants who have arrived in Canada and obtained their permanent residency or Canadian citizenship after 2010. Adopting Connelly and Clandinin's narrative inquiry, this study presents stories of participants with the hope to shed light on the process of Chinese immigrants becoming Canadian. Through these presentations, this study attends to the complex relationships participants have gone through in the process of negotiating their acceptance in Canada. Data gathered from two rounds of semi-structured interviews with six recent adult Chinese immigrants living in Ontario and Quebec were interpreted and discussed using Berry's acculturation framework, a critical multiculturalism framework and a Critical Race Theory (CRT) framework in three individual research articles. Findings reveal that Chinese immigrants actively participate in social, cultural, political and economic affairs in Canada. Their participation in Canadian society exhibits their acceptance of Canadian cultures and values while living in Canada. Based on these understandings, they offer their suggestions to make Canada a more democratic, just and livable place. Racism, discriminations and stereotypes received from some members of the host society have created tangible damage to their relationship with Canada. Nonetheless, Chinese immigrants express their desire to be included and integrated into Canadian society. These narratives from recent adult Chinese immigrants supplement literature in citizenship education. Policy makers, scholars, educators and the general public should listen to their stories and find more ways to include Chinese immigrants in the building of democracy and multiculturalism 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".