Bibliographic record
Abstract
Outcomes of appeals to high courts will depend in part on the ideological preferences of the justices who decide the appeals. The institutional structure of a high court may affect how far these preferences influence outcomes. The US Supreme Court, for example, hears almost all appeals en banc, which means that there is no opportunity to ‘game’ the outcome by choosing which justices hear the appeal. High courts in other countries such as Canada, the United Kingdom, Australia, and Israel, on the other hand, hear appeals in panels of varying sizes and therefore provide potential opportunities for the choice of panel composition to influence outcomes. However, differing panel sizes also provide the opportunity to use judicial resources more efficiently, such as by tailoring panel size to the importance or difficulty of the particular appeal. In the article, we examine both these potential uses of varying panel sizes using data on how chief justices of the Supreme Court of Canada chose panels over the period from 1954 to 2013. We find some evidence of strategic panel composition but the practical impact of such gaming is negligible. In order to examine non-strategic motivations for why chief justices choose different sizes of panels, we develop a model for optimal choice of panel size. The model suggests that, in the presence of scarce judicial resources, panel sizes can be deliberately adjusted to improve allocative efficiency. Using data from the Supreme Court of Canada over the 1954–2013 period, we uncover evidence consistent with our model’s prescriptions for optimal panel sizes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".