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Judicial Power in Russia: Through the Prism of Administrative Justice

2004· article· en· W2121312343 on OpenAlexaff
Peter H. Solomon

Bibliographic record

VenueLaw & Society Review · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsJurisdictionAdjudicationLawPolitical scienceEconomic JusticeImpartialityPrinciple of legalityState (computer science)DiscretionPower (physics)Exclusive jurisdictionJudicial independenceJudicial reviewOriginal jurisdictionSupreme court

Abstract

fetched live from OpenAlex

This article assesses the power of judges in Russia (on courts of general jurisdiction, arbitrazh courts, and military courts) in dealing with cases in which the government or one of its officials is a party. Power, that is, the resources of judges to make binding decisions, is understood as including jurisdiction, discretion, and authority to ensure compliance. The article analyzes the dramatic growth of jurisdiction and caseload in administrative justice in post-Soviet Russia to the year 2002 and examines how the courts have performed in handling the review of actions by officials (including in the military), tax cases, electoral disputes, and the legality of normative acts (both regulations and laws of lower governments), especially in the late 1990s. High rates of success for persons bringing suits against the government suggest that judges were able by and large to adjudicate fairly and rule against the state. To a considerable degree (but not always), those decisions were implemented (more often than were constitutional and commercial decisions). Interestingly, citizens who challenged the actions of officials in court had much more success than those who brought complaints to the Procuracy. Finally, the article develops an agenda for future research that would deepen understanding of the significance of administrative justice in the Russian Federation and the power of judges.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.075
GPT teacher head0.383
Teacher spread0.308 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations50
Published2004
Admission routes1
Has abstractyes

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