When Affirmative Action Disappears: Unexpected Patterns in Student Enrollments at Selective U.S. Institutions, 1990–2016
Bibliographic record
Abstract
Discussions of U.S. affirmative action policy assume that considering race in undergraduate admissions increases Black and Latinx student enrollments. We show that this assumption that affirmative action is linked to Black and Latinx student enrollments holds true for higher-status colleges and universities, but not institutions across the field of higher education. We use fixed effects modeling to analyze the association between a stated affirmative action admissions policy and enrollment trends for first-year students of different racialized backgrounds between 1990 and 2016 at 1,127 selective institutions. We find that, at the most selective institutions, stated policy usage was associated with increased Black student enrollments. However, at less selective institutions, policy usage was associated with decreased Black student enrollments and increased Non-U.S. resident enrollments. We also identify close-to-zero estimates of this relationship for enrollment trends of additional demographic backgrounds. We use these findings to elaborate the role of field-level status dynamics in racialized organizations theory. Paradoxically, U.S. American higher education’s contemporary racialized status order roughly consists of higher-status institutions that consider race in admissions but do not enroll racially heterogeneous cohorts, middle-status institutions that do not consider race but enroll racially heterogeneous cohorts, and lower-status non-selective institutions that enroll disproportionately high numbers of Black, Indigenous, and Latinx students.
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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 | 0.010 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".