Evidence About the Potential Role for Affirmative Action in Higher Education
Bibliographic record
Abstract
In two recent cases involving the University of Michigan (Gratz v. Bollinger and Gruttinger v. Bollinger), the Supreme Court examined whether race should be allowed to play an explicit role in the admission decisions of schools. The arguments made in support of affirmative action admission policies in these cases and others raise two fundamental questions. First, do students actually have incorrect beliefs about individuals from different races at the time of college entrance? Second, if students do have incorrect beliefs at the time of college entrance, can diversity on a college campus change these beliefs? While a small literature has recently shed some light on the second question, no previous work has been able to provide direct evidence about the first one. In this paper we examine the first question by taking advantage of unique data collected specifically for this purpose.
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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 | 0.053 | 0.150 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.041 | 0.002 |
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".