Racism and Xenophobia Towards China and People with Chinese Ethnicity Following COVID-19
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
This content analysis examined public reactions to Twitter posts made by Donald Trump referring to COVID-19 as the “Chinese Virus”. Fifty replies were open coded from which eight themes emerged: endangerment, stigmatization, xenophobia, accountability, accuracy, inferiority, visual promotion, and written promotion. The themes correspond to four meta-themes regarding China, its population, and people with Chinese ethnicity including: explicit opposition to racism towards China and its population, neither an opposition nor a promotion of racism, an implicit promotion of racism, and an explicit promotion of racism. The most prevalent theme addressed xenophobia and more specifically, an opposition to racism towards people with Chinese ethnicity. While most replies to Trump’s Tweets demonstrated an opposition to xenophobia, 14 of the 50 Tweets analyzed explicitly promoted racism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it