Understanding Anti-Asian Racism from Communication Perspectives: Insights from a Rapid Literature Review
Bibliographic record
Abstract
Background: Given the escalating anti-Asian racism and xenophobia caused by the COVID-19 pandemic, this Research in Brief presents a rapid review of relevant research published between March 2020 and February 2022 in cultural studies and communication journals. Analysis: The data collection identified only 13 articles published by the target journals, indicating the marginal status of communication and media studies in the expanding body of research on anti-Asian racism. Further qualitative thematic analysis of the 13 articles revealed their analytical emphasis on anti-Asian discourse and rhetoric online. Meanwhile, the structural factors underlying the reproduction of systemic racism remain underexplored. Conclusion and implications: Based on this rapid review, it is recommended that future research pay more attention to how racial tension and discrimination are woven into everyday communications across a range of media including social media, traditional media, and interpersonal communication. There is also an urgent need for communication scholars to develop intersectional lenses that facilitate the critical analysis of macro factors (class, gender, geopolitics, etc.) that contribute to the reproduction of racial hierarchy in Canada and other settler states.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".