Racial concentration and dynamics of COVID-19 vaccination in the United States
Bibliographic record
Abstract
This article considers how county-level concentrations of Asians, Blacks, Hispanics, and Whites are associated with COVID-19 vaccination differently. I argue that racially specific mechanisms-differential concentrations of social vulnerability and political ideology by race-are likely to create diverse associations between racial concentration and COVID-19 vaccination not only across racial groups but also within racial groups over time from early rollout to the time after COVID-19 vaccines became widely available. I test this argument by drawing on data from multiple sources that include county-level information on COVID-19 vaccination rates, racial population make-ups, and measures of political ideology and community vulnerability. Results show that the association between racial concentration and COVID-19 vaccination changes substantially across and within racial groups over time. Counties with higher percent of Asians and percent of Whites have higher vaccination rates at earlier time intervals whereas counties with higher percent of Latinos and percent of Blacks show lower vaccination rates. This trend flips at later dates for percent of Blacks, percent of Latinos, and percent of Whites. Results from multilevel regression models and mediation analysis controlling for vaccine hesitancy show that social vulnerability and political ideology are the underlying factors and their differential associations with diverse racial concentrations help create the racially specific and time-varying patterns.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".