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Record W2794396534 · doi:10.1163/15730352-04301003

Transparency in the Work of Judicial Councils: The Experience of (East) European Countries

2018· article· en· W2794396534 on OpenAlexaff
Peter H. Solomon

Bibliographic record

VenueReview of Central and East European Law · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and International Law Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransparency (behavior)AccountabilityPolitical scienceJudicial reviewJudicial activismJudicial independenceJudicial reformAutonomyOpenness to experiencePublic administrationLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.290
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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