Racial Diversity in U.S. Congregations, 1998–2019
Bibliographic record
Abstract
Abstract Racially diverse congregations have become an important part of the American religious landscape. We use data from the National Congregations Study (NCS), notably including data from the fourth wave, collected in 2018–2019, to examine 20 years of racial diversity in congregations. We find that racial diversity within congregations has increased substantially between 1998 and 2019. There are more congregations in which no one racial or ethnic group comprises more than 80 percent of the people, congregations’ average diversity level has increased, and the percentage of all‐white congregations has declined. Nearly a quarter of evangelical churches now have no one ethnic group constituting more than 80 percent of the people, a rate comparable to what we observe among Catholic churches. Moreover, congregations that meet this 80‐percent threshold are more likely to be led by black clergy in 2019 than they were in 1998. We end with a note of caution about concluding that diverse congregations necessarily promote racial justice.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".