Bibliographic record
Abstract
This Article explores which legal precedents judges choose to support their decisions.When describing the legal landscape in a written opinion, which precedent do judges gravitate toward? We examine the idea that judges are more likely to cite friendly precedent. A friendly precedent, here, is one that was delivered by Supreme Court Justices who have similar political preferences to the lower court judges delivering the opinion. In this Article, we test whether a federal Court of Appeals panel is more likely to engage with binding Supreme Court precedent when the political flavor of that precedent is aligned with the political composition of the panel. We construct a unique dataset of 591,936 citations to United States Supreme Court decisions by the federal Courts of Appeals in 127,668 unanimous decisions from 1971 to 2007. We find that judges gravitate toward friendly precedent. The political composition of a panel consistently influences which binding precedent is cited in the written opinion. All Republican-appointed panels gravitate toward the most conservative precedent; all Democratic-appointed panels gravitate toward the most liberal precedent and unfavorably cite the most conservative precedent. This result is notable because it provides strong evidence that judges, when reasoning their decisions, have different conceptions of binding precedent.
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".