Ethnoracial Diversity Across Nonmetropolitan and Metropolitan America: Urbanization, Multiracial Growth, and Uneven Exposure to Diversity
Bibliographic record
Abstract
High levels of ethnoracial diversity are understood as a defining characteristic of America’s metropolitan areas. In contrast, the role of diversity in nonmetropolitan areas is often underappreciated. In this study, we use census data from 1980 to 2020 to evaluate growing diversity in nonmetropolitan counties and to understand how diversity varies across metropolitan and nonmetropolitan counties. We measure levels of diversity (measured by Simpson’s Diversity Index), compare exposure to diversity across seven ethnoracial groups, and produce counterfactual estimates to measure how population growth (or decline) among each group has contributed to overall diversity. We find that diversity in nonmetropolitan counties has nearly doubled in the past forty years yet remains firmly below that of metropolitan counties in 2020, and that White and non-White exposure to diversity has converged substantially in recent years. Importantly, nonmetropolitan diversity is increasing due to both growing multiracial populations and declining White populations. Finally, we find that current and former nonmetropolitan counties that have experienced different forms of urbanization tend to have higher levels of diversity than the least urbanized nonmetropolitan counties.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".