Bibliographic record
Abstract
The Supreme Court justices are talking. And they are talking more than ever during oral argument. The term “hot bench” implies that appellate judges engage in vibrant verbal exchanges with the parties during oral hearings. As part of the new oral argument, Supreme Court justices now speak more while the parties speak less, they interrupt both their colleagues and the parties (especially women) more frequently than in the past, and some of their questions advocate for positions rather than seek information. A hot bench raises crucial concerns about the nature of oral argument and appellate judges’ role in a constitutional democracy. This Article addresses those concerns and advances a theory about the connection between a hot bench and appellate adjudication. It provides a new account of how active hearings can promote certain functionalist and democratic virtues of oral argument that cold benches and written decisions cannot. Appealing to asymmetric information theory in economics, this Article demonstrates how judges form majorities through signaling and screening. A more well-rounded account of a hot bench’s value, however, requires an examination of its vices as well as its virtues. This Article concludes by demonstrating why appellate judges must avoid particularly costly trade-offs and how they can do so.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".