Bibliographic record
Abstract
Judicial independence is increasingly viewed as a sine qua non of democratic constitutionalism. But in spite of a widespread consensus on the importance of having an independent judiciary, debates about the meaning of judicial independence persist in the literature. For scholars interested in comparative constitutional law, the uncertainty surrounding the definition of judicial independence is particularly vexing and raises several challenging questions: is there a universal set of conditions necessary for judicial independence? Or are there perhaps several models of a judicial independence? One issue that arises from these various questions is whether it is possible to develop a taxonomy of judicial independence. Although taxonomies inevitably produce an incomplete picture of the objects they classify, a taxonomy can assist comparative law scholars by providing an analytical framework for comparison. In Part 1 of this paper, the author explores the conceptual problems associated with finding a universal definition of judicial independence, arguing that there is no single, satisfactory definition of judicial independence. In Part 2 of the paper, the author addresses some of the methodological problems associated with developing a qualitative taxonomy. This is followed by a discussion of the organizing criteria that will be used to construct the four models of judicial independence found in the author’s proposed taxonomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".