Explaining Difference in the Quantity of Supreme Court Revisions: A Model for Judicial Uniformity
Bibliographic record
Abstract
While civil law supreme courts (e.g., Italy, France, Chile) hear up to 90% of the petitions for revisions, common law supreme courts (e.g., U.S., U.K, Canada) hear as low as 1% of the same type of cases. In this study we postulate that these different commitments towards revisions are each consistent with different approaches by which the legal system provides judicial uniformity. We formulate a theoretical model that shows that a given level of uniformity in lower (or appeal) court decisions can be achieved either by fixing a given probability of judicial revision or a given monetary/non-monetary disutility associated with a reversal. Hence, despite the fact that common law legal systems are characterized by a lower probability of case revision, we cannot state a priori that judicial uniformity is greater in civil law systems, as this will depend upon the magnitude of the disutility associated with a reversed decision. Indeed, with the exception of the impact upon career concerns (which net effect is not clear) in terms of ideology, reputation and legal standards, reversal disutility seems to be much higher in common law systems than in civil law systems. In addition, we demonstrate that in an efficient legal system the optimal number of revisions increases with the size of the reversal disutility, but decreases with the probability that the supreme court makes erroneous decisions; the total number of cases soliciting revision and the intrinsic utility obtained by a lower court which enforces its desired rule. We also show that in an efficient legal system it is the judicial law-making role of a common law supreme court that explains why that Court revises fewer cases than a civil law supreme court.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".