Beyond Multiculturalism: Identity and Discrimination Challenges of Chinese Canadian Communities
Bibliographic record
Abstract
Amidst the shifting tides of globalization and Canada’s celebrated commitment to multiculturalism, the Chinese Canadian community encounters the persistent and intertwined challenges of cultural identity formation and systemic racial discrimination. The surge in anti-Asian racism during the COVID-19 pandemic has not only intensified these challenges but has also exposed the inadequacies of Canadian multicultural policy in confronting deeply rooted structural inequalities. This study employs Critical Race Theory (CRT) to provide a rigorous and nuanced analysis of how cultural identity and discrimination intersect in the lived experiences of Chinese Canadians. Through an innovative mixed-methods approach that combines in-depth qualitative interviews and robust quantitative survey data, the research reveals the paradoxes and tensions inherent in multiculturalism: while fostering a sense of belonging and recognition, it too often fails to dismantle the institutional barriers faced by racialized minorities. The paper further examines the “Stop Asian Hate” movement as both a grassroots response and a catalyst for reimagining anti-racism strategies in Canada. Ultimately, the study advances the Sun Model of Anti-Racism and Multiculturalism (SMAM)—a novel, integrated framework that underscores the critical importance of policy reform, transformative education, and community empowerment in achieving genuine racial equity and social justice in an increasingly diverse Canadian society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.066 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".