Judge Gender and the Voting Behavior of Justices on Two North American Supreme Courts
Bibliographic record
Abstract
While there have been a number of previous studies examining the similarities and differences in the voting behavior of male versus female judges, few have attempted cross-national comparisons. Since the early 1980s, the Supreme Courts of both the United States and Canada have had at least one female justice sitting at all times. The first female justice in the United States and the first female justice in Canada are on record as having very different beliefs about the practical effect of gender diversification of appellate courts. The present study is the first analysis to explore which of those perceptions about the consequences of such diversification is consistent with the actual patterns of voting by the justices on both courts. We find that in Canada, there are substantial gender differences on many of the significant policy areas that produce divisions on the Court. However, in the United States, gender differences disappear when one controls for the political party of the justice.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".