Explaining Difference in the Quantity of Cases Heard by Courts of Last Resort
Bibliographic record
Abstract
Abstract While civil law courts of last resort—e.g., cassation courts in France, Italy, and Chile—review up to 90% of appealed cases, common law courts of last resort—e.g., supreme courts of the United States, United Kingdom, and Canada—hear as few as 1% of the same petitions. In this study, we postulate that these different policies can be explained by a comparatively larger commitment from common law courts of last resort to judicial law-making rather than judicial uniformity. While courts require few hearings to update the law (in theory one decision is sufficient), they need a large number of hearings to maximize consistency in the lower courts’ interpretation of the law. We show that the optimal number of hearings increases with an increment in the courts’ concern for uniformity. We also show that if hearing costs are linear then the hearing policies of all courts can be classified in only two types. In addition, we predict important changes in hearing policies when the number of petitions increases. Finally, we find that hearing rates and reversal disutility operate as two ways in which a legal system can achieve a given level of judicial uniformity.
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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 | 0.001 | 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 | 0.000 | 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".