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.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 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".