#StopAsianHate: A Critical Discourse Analysis of Anti-Asian Racism During the COVID-19 Pandemic in Online Canadian News Media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it