Bibliographic record
Abstract
Abstract This chapter begins by reviewing recent developments in law and courts research on judicial independence. It then highlights three areas where greater conceptual clarity would aid theory development and empirics: more clearly articulating the specific actors in question and their institutional powers; more specification of the expected utility calculations those actors make when it comes to independence; and more directly conceptualizing the threat those empowering courts face, and the attendant benefits independent courts provide. The following section observes a unifying feature of law and courts explanations for judicial independence: that the court in question has judicial review powers. Whether the causal logic is insurance, blame avoidance, or public support, explanations are all predicated on the ability of courts to use review powers—and their willingness to do so. Finally, the chapter assesses popular measures of judicial independence in light of this observation. Here, it shows that although judiciary measures from the Varieties of Democracy project purport to capture review, in practice they do not; further, even when it comes to what should be ‘easy’ cases, these measures lack face validity and fail to comport with insights from the literature regarding the judicialization of politics.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".