Addressing Western Asian alienation : exploring the application of multiculturalism in B.C. government messaging to address the rise of anti-Asian racism in B.C. during the COVID-19 pandemic
Bibliographic record
Abstract
Despite multiculturalism serving as a policy for more than 50 years in Canada and 30 years in British Columbia to build acceptance of cultural diversity, the COVID-19 pandemic has served as an impetus for a resurgence of anti-Asian racism in Canada, particularly in B.C. This study explores how the policy of multiculturalism has been applied in Canada’s westernmost province to address anti-Asian racism, examining its application from a communication perspective and its unique sub-national context. Using a pragmatic, mixed-methods approach, the study involved a survey of Canadians of Chinese, Japanese, and Korean backgrounds living in B.C., and a content analysis of B.C. government messaging related to multiculturalism and anti-Asian racism within the fiscal periods of 2017-18 and 2021-22. The study finds that the provincial government application of multiculturalism focuses on the fundamental acknowledgement of the existence and value of cultural and ethnic diversity rather than on a more advanced application promoting belonging and integration of immigrants into Canadian society. The findings suggest opportunities for government messaging to strengthen its focus on values related to inclusion that are of importance to Canadians of East Asian descent, and a need for the application of multiculturalism to focus on strengthening the sense of belonging between Canadians of all cultural backgrounds and ethnicities to collectively recognize the common values we share as a basis of a common civic identity.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".