Bibliographic record
Abstract
Consider this scenario: Two judges with parallel cases are each ready to issue an injunction. But their injunctions may clash, ordering incompatible actions by the defendant. Each judge has written an opinion justifying her own intended relief, but the need to avoid conflicting injunctions presses her to make a further choice – “Should I issue the injunction or should I stay it for now?” Each must make this decision in anticipation of what the other will do.\nThis Article analyzes such a judicial coordination problem, drawing on recent examples including the DACA cases and the “sanctuary cities” cases. It then proposes a solution: When faced with a possible clash of injunctions, each district judge should issue or stay her intended relief in accordance with the real-world outcome she thinks the majority of district judges would choose. Following such a shared convention, judges with diverse views will have a better chance of avoiding a clash because their estimates of the majority view are probably more similar than their individual views. And a stay would not signify abandoning a judge’s own views (which are still fully aired in her written opinion) but would instead reflect an awareness that other judges’ views may differ – akin to the existing practice of a stay pending appeal. Notable complications are addressed, including the first-mover advantage of the earliest judge to act; the role of the appeals courts; the possibility of circuit splits; and how such a shared convention might break down.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".