Contentious Policies: The Experiment with Affirmative Action in Undergraduate Admissions to Public Universities
Bibliographic record
Abstract
Affirmative action policies have polarized the American public for over a quarter of a century. With regard to undergraduate university admissions, the Department of Education has not issued a definitive policy stance and has chosen to rely upon the results of previous and forthcoming research. Most scholars have not seized the opportunity to explore the effectiveness of affirmative action on a university's minority admission or enrollment rates. Additionally, scholars have not established the role that other confounding factors, such as financial aid and academic preparation, play in determining admission or enrollment rates. This research explores the role of affirmative action policies and percentage plans in determining the admission and enrollment rate of African Americans and Hispanics at the University of California and the State University System of Florida. Results indicated that affirmative action increased the admission rates of the three underrepresented minority groups while it decreases the enrollment rates of same groups in California. The amount of financial aid was also statistically significant when used to determine a minority group's admission or enrollment rate. In the Florida case, affirmative action was a factor in determining undergraduate admissions and enrollment rates. However, the models did not have the explanatory power of the California models. These findings have substantial implications for current public policy as the U.S. Supreme Court will consider two lawsuits against the University of Michigan and its various admissions policies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".