Language, Culture, and Identity: Affirming Asian Canadian Identities in Mandarin Bilingual Classrooms
Bibliographic record
Abstract
This qualitative study explored how racial stereotypes and anti-Asian racism were addressed in Alberta’s Mandarin bilingual programs. Seventeen semi-structured interviews were conducted on Zoom with Chinese Canadians who graduated from high school between 2018 and 2023. Analysis of the interviews were informed by Critical Race Theory in Education, Culturally Responsive Pedagogy and Asian Critical Theory. Findings suggest the integration of Chinese language and culture in Mandarin bilingual classrooms help Chinese Canadian students foster a strong sense of belonging in school. Unlike their Chinese Canadian counterparts enrolled in non-bilingual programs, students in bilingual programs feel less pressure to assimilate into western Canadian culture and often retain both a connection to and pride for their Asian heritage. For students who were of Han descent, their ethnicity was also rarely a source of contention. However, students of multi-racial or Chinese minority backgrounds often struggled with trying to fit into the program’s dominant culture and norms. Racial stereotypes associated with the Model Minority Myth were also prominent in the program. The stereotype that all Asians naturally get “good grades” created academic pressure for many students. However, findings also reveal the diverse ways students challenge the Model Minority Myth and its assertion that all Asian students are high academic performers. For some students, they actively rejected this stereotype by striving for balance between their academic success and mental well-being. Meanwhile, due to the creation of a “safer space” for Chinese students within the program, the recent rise in anti-Asian racism during the COVID-19 pandemic was the first time many students encountered racism. Fortunately, participants responded to racialized bullying with humour among their friends to mitigate its impact. They also reclaimed and redefined their identities as Asian Canadians in response to racial stereotypes. Ultimately, despite the room for further improvement, the Mandarin bilingual program has created a unique cultural pocket where many Chinese Canadian students feel validated and empowered. These collective stories point to the importance of heritage language retention, culturally responsive teaching, and representation for visual minority students in Canadian classrooms. It also points to the creation of a new culture that necessitates further research.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".