Transparency in the Work of Judicial Councils: The Experience of (East) European Countries
Bibliographic record
Abstract
In many countries of Europe, including especially its Eastern part, in the 21st century judicial councils have replaced ministries of justice as the bodies that manage judicial careers and govern the judiciary. This model may enhance the autonomy of the judicial branch but also weaken its accountability and lead to judicial corporatism. One way to counter the negative trends is to enhance public accountability of judicial councils, by making the work of councils is open and visible. Not surprisingly, judicial reformers have made transparency into a key criterion for a successful judicial council, leading many countries to promote transparency in their judicial councils. This article evaluates this trend−by (1) providing cases studies of four judicial councils, those of Italy, Poland, Moldova, and Latvia; and (2) comparing the work of empowered judicial councils throughout Europe with regard to the openness of judicial disciplinary hearings and public sessions of judicial councils themselves. On this basis I argue that while legal requirements for transparency are becoming the norm, they do not necessarily make the work of judicial councils open, let alone produce public accountability. This outcome requires as well a genuine commitment of council members and staff to transparency arrangements, the cessation of resistance to such arrangements, and the provision of money and staff to support them.
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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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".