THE GROWING CORRELATION BETWEEN RACE AND SAT SCORES: NEW FINDINGS FROM CALIFORNIA
Bibliographic record
Abstract
This paper presents new and surprising findings on the relationship between race and SAT scores. The findings are based on the population of California residents who applied for admission to the University of California from 1994 through 2011, a sample of over 1.1 million students. The UC data show that socioeconomic background factors – family income, parental education, and race/ethnicity – account for a large and growing share of the variance in students’ SAT scores over the past twenty years. More than a third of the variance in SAT scores can now be predicted by factors known at students’ birth, up from a quarter of the variance in 1994. Of those factors, moreover, race has become the strongest predictor. Rather than declining in salience, race and ethnicity are now more important than either family income or parental education in accounting for test score differences. It must be cautioned that these findings are preliminary, and more research is needed to determine whether the California data reflect a broader national trend. But if these findings are representative, they have important implications for the ongoing debate over both affirmative action and standardized testing in college admissions.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 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".