#StopAsianHate: A Critical Discourse Analysis of Anti-Asian Racism During the COVID-19 Pandemic in Online Canadian News Media
Bibliographic record
Abstract
In late January 2020, the first confirmed case of the COVID-19 virus was verified in Canada (Marchand-Senecal, Kozak, Mubareka, Salt, Gubbay, Eshaghi, Allen, Li, Bastien, Gilmour, Ozaldin & Leis, 2020). In early March 2020, the World Health Organization (WHO) officially declared the COVID-19 virus as a global pandemic at a media briefing (World Health Organization, 2020). The advent and evolution of the COVID-19 pandemic has created a culture of enhanced public health and safety measures. In addition, a dramatic increase in anti-Asian discrimination and racism due to the COVID-19 pandemic has also materialized in Canada (Statistics Canada, 2020). At an unprecedented time, the media has become a critical and powerful mechanism in order to remain informed about emerging events, including anti-Asian discrimination and racism in Canada. Therefore, the purpose of the study was to explore the differences and similarities between the discourses of anti-Asian racism during the COVID-19 pandemic in online Canadian news media. A critical discourse analysis of 30 news articles from Vancouver-based and national online news sources was conducted, which revealed several themes about the relationship between Asian Canadians, racism, and media amidst the COVID-19 pandemic. Department: Honours Sociology Faculty Mentor: Dr. Kalyani Thurairajah
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".