QUALITY ASSESSMENT OF JUDGES WORK IN THE CONTEXT OF ANTITRUST REGULATION
Bibliographic record
Abstract
The issue of assessing the quality of judicial decisions is particularly relevant for Russia as for a country at a transitional stage of institutional development. The paper analyzes the factors of quality of judicial decisions through antitrust cases in relation to international practice and Russian specifics. There is an analysis of the main features that determine the quality of the decisions made by the judges on the basis of a unique database of commercial courts cases. The article notes the high significance of the level of specialization and economic competence of judges when considering cases of a certain type. The decision quality factor is based on the minimization of law enforcement errors; and it is determined by such indicators as a fact of appeal, equality of decisions of first and higher instances, as well as a cumulative number of instances considering a case as a parameter of the quality of the case consideration by the judicial system in general. There are several groups of parameters aff ecting the quality. This research is especially focused on individual characteristics of judges, complexity of the approach required to the analysis of a potential violation and sanctions imposed on a party. It is shown that specialized experience of a judge in consideration of cases of a certain type provides rather intensive impact on the decision which will not be canceled by higher instances.
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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 | 0.028 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".