Taking a Dip in the Supreme Court Clerk Pool: Gender-Based Discrepancies in Clerk Selection
Bibliographic record
Abstract
Former U.S. Supreme Court clerks are heavily recruited by select law firms, and many eventually find their way to policy “elite” positions in the government or in the legal academy. A number of former clerks have returned to the Court as litigators, and a subset has returned to the Court as Justices. We are interested in clerk selection for two reasons. First, clerks influence key aspects of the judicial process while serving in their clerkship capacity, and second, many seem to be in a good position to influence legal policy well after their clerkships have ended. With this in mind, it is natural to ask about the selection of such individuals to these posts. There are larger questions of diversity, however, that have permeated discussions of the Court’s clerkship selection practices. In this Article, we explore one critical dimension—the relative imbalance between men and women serving as High Court clerks. We analyze the U.S. Supreme Court directly, but also supply comparison points in assessing clerkship diversity in Canada and Brazil.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".