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Record W4409974892 · doi:10.58970/ijsb.2591

Beyond Multiculturalism: Identity and Discrimination Challenges of Chinese Canadian Communities

2025· article· en· W4409974892 on OpenAlexaboutno aff
Peng Sun, Boxi Zuo

Bibliographic record

VenueInternational Journal of Science and Business · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismIdentity (music)SociologyGender studiesPolitical scienceAestheticsArtPedagogy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.307
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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