The Legal Academy’s Ideological Uniformity
Why this work is in the frame
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Bibliographic record
Abstract
We study the ideological balance of the legal academy and compare it with the ideology of the legal profession more broadly. To do so, we match professors listed in the Association of American Law Schools’ Directory of Law Teachers and lawyers listed in the Martindale-Hubbell directory to a measure of political ideology based on political donations. We find that 15 percent of law professors, compared with 35 percent of lawyers, are conservative. This may not simply be due to differences in their backgrounds: the legal academy is still 11 percentage points more liberal than the legal profession after controlling for several relevant individual characteristics. We argue that law professors’ ideological uniformity marginalizes them but that it may not be possible to improve the ideological balance of the legal academy without sacrificing other values.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it