Coethnic Concentration and Asians’ Perceived Discrimination across U.S. Counties during 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
Aggregate figures unequivocally depict an increase in anti-Asian sentiment in the United States and other Western countries since the start of the COVID-19 pandemic, but there is limited understanding of the contexts under which Asians encounter discrimination. The authors examine how coethnic concentration shapes Asians' experiences of discrimination across U.S. counties during COVID-19 and also assess whether county-level context (e.g., COVID-19 infection rates, unemployment rates) could help explain this relationship. The authors analyze the Understanding Coronavirus in America tracking survey, a nationally representative panel of American households, along with county-level contextual data. The authors find an n-shaped relationship between coethnic concentration and Asians' perceived discrimination. This relationship is explained largely by county-level COVID-19 infection rates. Together, the context of medium Asian concentration and high COVID-19 cases created a particularly hostile environment for Asians during COVID-19.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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