Differential Prediction in the Use of the SAT and High School Grades in Predicting College Performance: Joint Effects of Race and Language
Bibliographic record
Abstract
The literature on differential prediction of college performance of racial/ethnic minority students for standardized tests and high school grades indicates the use of these predictors often results in overprediction of minority student performance. However, these studies typically involve native English‐speaking students. In contrast, a smaller literature on language proficiency suggests academic performance of those with more limited English language proficiency may be underpredicted by standardized tests. These two literatures have not been well integrated, despite the fact that a number of racial/ethnic minority groups within the United States contain recent immigrant populations or heritage language speakers. This study investigates the joint role of race/ethnicity and language proficiency in Hispanic, Asian, and White ethnic groups across three educational admissions systems (SAT, HSGPA, and their composite) in predicting freshman grades. Our results indicate that language may differentially affect academic outcomes for different racial/ethnic subgroups. The SAT loses predictive power for Asian and White students who speak another best language, whereas it does not for Hispanic students who speak another best language. The differential prediction of college grades of linguistic minorities within racial/ethnic minority subgroups appears to be driven by the verbally loaded subtests of standardized tests but is largely unrelated to quantitative tests.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".