Judging Alone: Reflections on the Importance of Women on the Court
Bibliographic record
Abstract
“The word I would use to describe my position on the bench is lonely.” So said Justice Ruth Bader Ginsburg in 2007, when asked to comment on her position on the U.S. Supreme Court after the resignation of Justice Sandra Day O'Connor. After a year as the Court's only woman, Ginsburg had begun to feel the solitude that comes from judging alone, being the Court's only descriptive and often symbolic representative of women's interests. Ginsburg's position was not, sadly, as rare as we might hope in industrialized democracies. Although some countries, such as Canada, have had near majorities of women on their respective high courts, other countries, such as the United Kingdom, continue to have only one woman on their national tribunals.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.071 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 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".