Bibliographic record
Abstract
This article takes a rare look at the extrajudicial measures, or ‘non-adjudicative strategies,’ which judges of domestic courts can take to build their institution. Although much has been written about the strategies that judges deploy within the adjudicative context, little is known about how judges can strengthen themselves institutionally outside the courtroom. This article identifies the use of non-adjudicative strategies as a global phenomenon and offers a systematic appraisal of the non-adjudicative strategies that judges can deploy and when they should consider using them. Drawing on comparative examples from different regions and regime types, as well as synthesizing insights from a range of fields, the article offers a double typology, breaking down non-adjudicative strategies by form and by function in order to better grasp the variety of strategies available and their effects. It also reflects on the risks of adopting these strategies and develops standards for evaluating the legitimate uses of non-adjudicative strategies. The article encourages reflection on how judges should address contemporary institutional challenges and conceptions of the judicial role.
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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 | 0.034 | 0.175 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".