Mapping Anti-Asian Xenophobia: State-Level Variation in Implicit and Explicit Bias against Asian Americans across the United States
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
Using national data from Project Implicit, the authors examine state-level variations in implicit and explicit bias against Asian Americans held by non–Asian Americans ( n = 196,678) from 2018 to 2022. The authors also explore state-level sociodemographic correlates of both types of bias. The findings reveal considerable heterogeneity in implicit and explicit bias across states. Moreover, Republican and swing states had higher levels of implicit bias against Asian Americans, and states with older median ages and greater percentages of Asian populations were associated with less explicit bias. This study underscores the importance of state-level variation in and structural factors of biases against Asian Americans as contexts for examining attitudes toward Asian Americans.
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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 | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it