Warning: Symptoms May Include Racism: A Content Analysis of Anti-Asian Racism and Sentiment Amid the COVID-19 Pandemic in Digital Media
Bibliographic record
Abstract
This study examined themes present in selected news articles which actively discussed anti-Asian racism and sentiment amid the COVID-19 pandemic. A content analysis was conducted on purposively sampled news articles from various media sources on the search engine, Google, under the “News” section. In total, 20 news articles were examined for how media framed the rise of anti-Asian racism and sentiment during the pandemic. Findings revealed little information on the rise and racialization of the COVID-19 outbreak, but there was a larger tie to the incidents of anti-Asian racism and sentiment and its relationship to the COVID-19 pandemic, the history of racism endemic to North American history, and recent remarks made by President Donald Trump, racializing COVID-19 as the “Chinese virus.” Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Diane Symbaluk Department: Sociology
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How this classification was reachedexpand
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".