EXPRESSIVE ASSOCIATION AND THE IDEAL OF THE UNIVERSITY IN THE SOLOMON AMENDMENT LITIGATION
Bibliographic record
Abstract
In this article, Professors Wolff and Koppelman offer a critical analysis of the free speech claims that were asserted by the law schools and law faculty that sought to challenge the Solomon Amendment. Solomon is a federal statute that requires law schools to grant full and equal access to military recruiters during the student interview season. The military discriminates against gay men and lesbians under its “Don’t Ask, Don’t Tell” policy, and the law professors claimed a right to exclude the military under the First Amendment doctrine of “expressive association,” arguing that the presence of discriminatory recruiters would interfere with the ability of faculty to express their own message of inclusion toward their gay students. Those claims were ultimately rejected by the Supreme Court inRumsfeld v. FAIR. Wolff and Koppelman argue that the law professors' litigation efforts, though well intentioned, were deeply misguided, seeking to extend a recent and aberrational decision in the law of expressive association to unsustainable lengths and, in the process, offering a characterization of the manner in which faculty engage in their own expression that is inconsistent with the ideals that should govern institutions of higher learning.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.065 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".