How Right-Wing Extremism Uses the COVID-19 Pandemic: Focusing on Anti-Asian Rhetoric
Bibliographic record
Abstract
The COVID-19 pandemic has provided an opportunity for right-wing extremism to legitimize its claims and promote anti-Asian rhetoric. This has led to an increase in hate crimes against Asian and Asian descendants in the US, UK, and Canada. Online platforms such as Twitter, Facebook, 4Chan, and Telegram have been used to spread disinformation and conspiracy theories and frame the crisis to promote the agenda of building a “white ethnostate.” Civil society and experts have expressed concerns that this anti-Asian rhetoric will normalize right-wing extremist ideology and increase its social legitimacy. It is important to understand that this rhetoric is being used to promote extremist ideology and recruit more members and that further research is needed to prevent further tragedies. The COVID-19 pandemic has had a multidimensional impact on Asians, and it is crucial to not overlook the growing anti-Asian sentiments in both online and offline spaces and to research the connection between right-wing extremist groups and radicalized individuals in order to tackle their harmful activities.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".