Bibliographic record
Abstract
Judicial decision-making is ideally impartial. In reality, judges are influenced by many different factors, including institutional context, ideological commitment, fellow justices on a panel, and personal preferences. Empirical literature in this area increasingly analyzes this complex collection of factors in isolation, when a larger sample size of comparative institutional contexts can help assess the impact of the procedures, norms, and rules on key institutional decisions, such as how appeals are decided. This book explains how the answers to the following institutional questions largely determine the influence of political preferences of individual judges and the degree of cooperation among judges at a given point in time. Who decides how judicial appointments are made? How does an appeal reach the court; what processes occur? Who is before the court; how do the characteristics of the litigants and third parties affect judicial decision-making? How does the court decide the appeal; what institutional norms and strategic behaviors do the judges follow in obtaining their preferred outcome? The authors apply these four fundamental institutional questions to empirical work on the supreme courts of the United States, UK, Canada, India, and the High Court of Australia. The ultimate purpose of this book is to promote a deeper understanding of how institutional differences affect judicial decision-making, using empirical studies of supreme courts in countries with similar basic structures but with sufficient differences to enable meaningful comparison.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".