How institutions structure judicial behaviour: An analysis of Alarie and Green’s <i>Commitment and Cooperation on High Courts: A Cross-Country Examination of Institutional Constraints on Judges</i>
Bibliographic record
Abstract
No theory of judicial behaviour ignores institutions, but, all too often, their role in structuring judges’ choices goes assumed rather than directly evaluated. For this reason alone, we should applaud Benjamin Alarie and Andrew J. Green. Not only do they take institutions seriously; they attempt cross-national assessments of their effect on judging. This is their book’s overarching contribution, but there are many others along the way – so many that Commitment and Cooperation on High Courts is bound to take its place among the classics in the ever-growing field of judicial behaviour. My aim is to bring Alarie and Green’s contributions into relief by highlighting their key arguments and empirical results. Along the way, I integrate some of the existing literature if only to show where and how the authors advance our understanding of judging. All of this amounts to Parts I and II. But a simple summary of Commitment and Cooperation will not suffice because Alarie and Green invite the reader to think about extensions. In that spirit, Part III offers some suggestions for forward movement.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".