Bibliographic record
Abstract
Abstract This book examines the courts of the Russian Federation under President Putin, how they work in practice, and what shapes the behaviour of its judges. It stresses the dual nature of a judicial system, where ordinary cases are mostly handled fairly but where cases of interest to powerful persons are subject to influence—a common situation in authoritarian states. The authors trace the origins of some contemporary practices to the Soviet past but also identify novelties. They pay attention to the struggles of reformers to make the courts fairer and more efficient, along with the measures taken to ensure that judges conform to the expectations of their political masters. This means dealing with the evolution of judicial governance, including the selection, promotion, and disciplining of judges. In studying the operation of the courts, the authors take a socio-legal approach, emphasizing how different players (petitioners, respondents, lawyers, prosecutors, accused, judges) behave and why. They deal with justices of the peace through to the Supreme and Constitutional Courts and detail the handling of civil disputes, criminal cases, business disputes, administrative justice (claims against state officials), and constitutional matters. They also examine the relation of the public to the courts, including its readiness to litigate disputes despite generally negative views of the courts. This analysis is as up to date as possible, including both the Constitutional Amendments of 2020 and developments relating to the first months of the 2022 war in Ukraine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".