Rankings without U.S. News: A revealed preference approach to evaluating law schools
Bibliographic record
Abstract
Abstract Since their inception in 1989, the U.S. News & World Report law school rankings have influenced how schools, students, and the legal profession itself think about legal education. In the Fall of 2022, however, several of the most selective law schools formally withdrew from the annual rankings. In so doing, these schools laid bare longstanding criticisms of the rankings' questionable criteria and opaque methodology. While the long‐term effect of this boycott remains to be seen, school rankings are likely here to stay. In this Article we design a more informative approach to rankings, based on actual decisions students make. Using individual‐level data provided by the Law School Admissions Council (LSAC), we analyze the universe of applicants to U.S. law schools for the period 1988 through 2017. In so doing, we are the first to create a revealed preference ranking based solely on where applicants matriculate given offers of admission. Our approach relies neither on potentially faulty data collection from schools nor arbitrary decisions about which factors to emphasize in rankings, thereby minimizing the scope for manipulation. It also allows us to quantify the magnitude of differences in preferences among schools and to test their statistical significance. Matriculants reveal a strong preference for a handful of the most selective schools; outside of the top tier, however, matriculants do not appear to draw meaningful distinctions between schools ranked adjacently or even near to each other. While existing school rankings sow more confusion than clarity, our analysis provides a rigorous and transparent alternative, and a blueprint for redesigning school rankings.
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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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".