Covering: Mutable Characteristics and Perceptions of Voice in the U.S. Supreme Court
Bibliographic record
Abstract
The growing emphasis on “fit” as a hiring criterion introduces the potential for a new, subtle form of discrimination (Bertrand & Duflo, 2017). Analysis of 1,901 U.S. Supreme Court oral arguments from 1998 to 2012 documents that voice-based snap judgments predict court outcomes. Male petitioners who rank below median in perceived masculinity are 7 percentage points more likely to win. This negative correlation between perceived masculinity and winning cases in the Supreme Court is more pronounced in masculine industries. Perceived femininity of women lawyers also predicts court outcomes. Democrats favor men with less masculine-sounding voices. Perceived masculinity explains additional variance in Supreme Court decisions beyond what is predicted by the best random forest prediction model. A de-biasing experiment using information and incentives in factorial design is consistent with misperceptions and taste for masculine-sounding lawyers explaining the negative correlation between perceived masculinity and Supreme Court wins.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".