100% of first-gen and veterans receive aid
Bibliographic record
Abstract
The School of Law is pleased to share that once again 100% of the first-generation college graduates and military veterans enrolled in its Juris Doctor program are receiving aid. First-generation students are supported by more than 20 scholarship funds including the First-Start Scholars Program established by 1982 alumna Kathelen V. Amos and the Daniel P. Amos Family Foundation. This initiative has grown from first-generation students in the 2019 J.D. entering class receiving a scholarship to every first-gen J.D. student getting aid for the third year in a row. Student veterans benefit from the Butler Commitment, which was created in 2019 by renowned trial attorney and 1977 law school alumnus James E. "Jim" Butler Jr. and guarantees aid to all enrolled military veterans. The School of Law is redefining what it means to be a great national public law school by offering a world-class, hands-on, purpose-driven educational experience with a pioneering commitment to accessibility and affordability, according to Dean Peter B. "Bo" Rutledge. Some recent statistics supporting this statement include: #1 or 2 Best Value Law School for the last six years (National Jurist). Nearly 99% Ultimate Bar Passage rate for Class of 2020 graduates. 50% reduction in aggregate annual student borrowing compared to 2013. 87% of students receiving scholarships (2021-22). 18 clinics and externships, with 94% participation (Class of 2023).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.085 | 0.032 |
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".