Racial Polarization in Sponsorship of United States Civil Rights Legislation
Bibliographic record
Abstract
Who has supported the policy interests of historically marginalized groups in the U.S. over the last 50 years? We test this question by creating a dataset of 202,775 House of Representatives bills from 1973 – 2022, and using it to track who sponsors bills protecting the civil rights of minority groups. We find that the total volume of civil rights legislation has remained stable over the last 50 years, but there have been contrasting trends beneath the surface. As racial minorities have gained more seats in Congress, minority legislators have shown greater commitment to sponsoring civil rights bills. In contrast, White legislators, who still hold most seats in Congress, have sponsored fewer civil rights bills, even controlling for overall productivity. This negative trend is strongest among White Republicans, but White legislators from both parties have sponsored fewer civil rights bills relative to minority legislators over time. These contrasting trends have created a racial divide which overshadows party differences. White legislators sponsored nearly 100% of civil rights bills in the early 1970s, but now sponsor approximately 40% while holding approximately 75% of House seats. We test several plausible reasons why the average White legislator is sponsoring fewer civil rights bills, but find no empirical support for any of them. The trend cannot be explained by controlling for the diversity of Congress, the districts where White legislators have been re-elected, or voter preferences. Racial polarization has been starkest in the last 10 years and shows no signs of abating.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".