The Mismatch Hypotheses in Law School Admissions
Bibliographic record
Abstract
The match effects for five different student populations Native American Asian Black Hispanic and White are gathered and analyzed to determine whether these effects provide support for the match hypotheses with respect to law school grades graduation and bar passage While some evidence was found supporting the negative match hypothesis for Black and Asian law school students in the lower propensity range the match effects for bar passage in the upper range were much lower than Richard SanderÔÇÿs reports and did not approach statistical significance Moreover no negative match effects for graduation were apparent Thus the bar passage rates difference seems very modest relative to the substantial social networking advantages of elite school attendance Because the variables most strongly associated with higher levels of leadership are attainment of a bachelorÔÇÿs BA degree at Harvard Yale and Princeton and attainment of a law degree the implication is that leadership potential which may fall outside the bounds of traditional admission criteria is more suitably described as a benefit to society Even Sander noted that he did not consider perhaps the single greatest benefit of affirmative action in law school its role in building the longterm careers of Black lawyers placing them in the most elite ranks of the profession and American society
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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 | 0.017 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.003 |
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".