Specific features of the legal regulation of prosecution for contempt of court: judicial rules established in different countries
Bibliographic record
Abstract
The purpose of the article is to reveal the specific features of prosecution for contempt of court in different countries. The methodological basis of this research is a set of general scientific methods (dialectics, abstraction, generalization, analysis, modelling) and special methods of scientific cognition (comparative and legal method, etc.). The existing types of responsibility and penalties for committing contempt of court in different countries of the world have been characterized. The authors have carried out the analysis of the experience of legal liability for manifestation of contempt of court rules established in the United States, Canada, France, Australia, Belgium, Poland, Great Britain, New Zealand, Ireland and India, which allowed to highlight the positive provisions for improvement of legislation in this area. It has been concluded that the purpose of establishing the aforementioned responsibility is to guarantee the administration of justice and the rule of law, maintain and strengthen public confidence in the judicial system, safeguarding the continuity of the judicial process. Based on the analysis of regulatory legal acts and the jurisprudence of several countries in the world, the authors have made the classification by categories of actions that qualify as contempt.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".