Rethinking US Census racial and ethnic categories for the 21st century
Bibliographic record
Abstract
Racial and ethnic categories in the US census have continually changed. In this paper, we address the question: How do high levels of immigration and a growing multiracial population challenge census racial and ethnic categories? We examined data from the 2000 Census 5 percent IPUMS to compare racial responses of native- and foreign-born Hispanics, Asians, and Middle Easterners, and native-born multiracial Hispanics, Asians, and Middle Easterners, by ancestry. The relationship between race and ancestry can be instructive. If people understand and identify with census racial categories, we expect considerable overlap between their reported race and ancestry. For some groups, including Europeans, Africans, and Middle Easterners (regardless of nativity) and foreign-born Asians, ancestry and race overlapped well. A serious challenge to current census racial categories is the large and growing numbers of people who reported Some Other Race (SOR) alone (primarily non-Cuban Hispanics) or in combination with another race (a diverse population that includes multiracial Hispanics, Middle Easterners, and Asians). One way of addressing this problem is to merge the current race and Hispanic questions, drop the SOR category, and add the ancestry question to the short-form census, changes that may more effectively meet statistical, government, and other needs.
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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 | 0.044 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".