Covering: Mutable Characteristics and Perceptions of Voice in the U.S. Supreme Court
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| 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) | 0.000 | 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 it