How Affirmative Action Context Shapes Collegiate Outcomes at America’s Selective Colleges and Universities
Bibliographic record
Abstract
During the 1990s and early 2000s, the affirmative action context in the United States changed. Affirmative action in higher education was banned in several states, and the Supreme Court ruled in Grutter (2003) that affirmative action, while constitutional, should be implemented via holistic evaluation of applicants. In this article, we use two datasets to examine how affirmative action context relates to academic outcomes at selective colleges and universities in the United States before and after the Grutter decision and in states with and without bans on affirmative action. Underrepresented minority students earned higher grades in the period after the Grutter decision than before it, indicating that the holistic evaluation method required by Grutter may enhance educational outcomes for these students. In contrast, we find no support for the idea, proposed by critics of the policy, that banning affirmative action leads to better collegiate outcomes for Black and Latino students at selective institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".