Female Justices, Feminism, and the Politics of Judicial Appointment: A Re-Examination
Bibliographic record
Abstract
In recent years, feminists in the United States have consistently advocated for the appointment of more female justices to the Supreme Court. Given the records of Justices O’Connor and Ginsburg on the Court and broader empirical findings below the Supreme Court level showing a relationship between a judge’s gender and her voting behavior, feminists have argued that, from a feminist perspective, the appointment of new female justices to the Court is likely to offer significant substantive, as well as symbolic, benefits. This Article challenges such feminist orthodoxy by showing that it is based on a mistaken view of existing empirical data on judicial behavior and its likely future predictive value. The article shows how, from both a quantitative and qualitative perspective, the current literature on judicial behavior in fact reveals little if any meaningful connection between a judge’s gender and her pro-feminist views, in a jurisprudential sense. By drawing on comparative experience in Canada, which between 2005 and 2008 had a female majority on its Supreme Court, the Article also shows how any female-feminist connection previously evident in the United States, particularly at a Supreme Court level, is unlikely to endure in the future, given changes in the kind and degree of discrimination experienced by female justices prior to appointment. Consequently, the Article also calls for a change in strategy on the part of feminists to focus more directly on the demonstrated jurisprudential commitments, rather than on the gender, of future judicial nominees.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".