Identity during a Crisis: COVID-19 and Ethnic Divisions in the United States
Bibliographic record
Abstract
During a crisis, does a community's ethnic composition influence policy efficiency? How do the effects of ethnic divisions differ from those of ethnic diversity? Despite the large body of work which considers ethnic composition, little attention has been given to how it matters for crisis-response policy. Using the lens of the COVID-19 pandemic in the United States, we show that ethnic divisions, rather than ethnic diversity, significantly reduce the efficacy of crisis response. U.S. counties with high levels of ethnic divisions fared worse than their less-divided counterparts after lockdowns in both COVID-19 cases and related deaths. Ethnic diversity had little effect, except in areas with high racial segregation. Crisis-response policies led to smaller mobility reductions and less mask-wearing in ethnically divided counties. These results are not driven by a lack of physical public goods, socioeconomic differences, or by the prevalence of high-risk populations. Findings are robust to various strategies of causal identification and falsification checks. Our results suggest that policies promoting ethnic and racial integration can allay the negative social and economic impacts of crises.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".