Crimes against Justice under the Legislation of the States of the European Union
Bibliographic record
Abstract
The countries of the European Union (EU) are united, but above all, each country is autonomous. EU Member States have different legislation on criminal offences. The EU authorities have already suggested the possibility of creating a single system for regulating legal provisions on criminal offences. Studying and comparing the legal systems and responsibilities for crimes against justice in individual countries will facilitate the analysis of the differences in the legislation of the EU countries. The purpose of this paper is to investigate crimes against justice in accordance with the laws of each individual European country. The paper considers the composition of such crimes, as well as the responsibility to which offenders can be brought in case of such crimes. The study uses the methods of analysis and synthesis, analyses legal provisions. General methods of scientific cognition used in this study include dialectical, historical, the Aristotelian method, method of systematic data analysis, formal legal method, method of legal modelling and comparative legal method. The study investigates the legal framework of European countries, in particular criminal codes and laws. This study systematises and groups the received information and data on criminal liability of judges for unlawful decisions. The European practices in punishing those who do not comply with court rulings and judgments are also analysed. A study of the legal system in individual EU countries will help distinguish between positive and negative aspects in the legislation. In addition, this study allows to consider and analyse the most effective laws, provisions, and principles that can be implemented in the current legal system of different countries of the world.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".