Enduring Hierachies in American Legal Education
Bibliographic record
Abstract
Although much attention has been paid to U.S. News & World Report's rankings of U.S. law schools, the hierarchy it describes is a long-standing one rather than a recent innovation. In this Article, we show the presence of a consistent hierarchy of U.S. law schools from the 1930s to the present, provide a categorization of law schools for use in research on trends in legal education, and examine the impact of U.S. News's introduction of a national, ordinal ranking on this established hierarchy. The Article examines the impact of such hierarchies for a range of decision making in law school contexts, including the role of hierarchies in promotion, tenure, publication, and admissions; for employers in hiring; and for prospective law students in choosing a law school. This Article concludes with suggestions for ways the legal academy can move beyond existing hierarchies, while still addressing issues of pressing concern in the legal education sector. Finally, the Article provides a categorization of law schools across time that can serve as a basis for future empirical work on trends in legal education and scholarship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".