AGREEMENTS IN CRIMINAL PROCESSES: PROBLEMS OF APPLICATION AND DEVELOPMENT
Bibliographic record
Abstract
Purpose of Study: In this paper, institutions of agreements (mediation) in criminal proceedings in various states were investigated regarding the history of their occurrence and development. The aspects under the study included features related to the use of institutions of agreement in individual countries (USA, Canada, Germany, Russia, Moldova, etc.); the regulatory framework of these countries, statistics on the use of institutions of agreements (mediation), as well as programs used as mediation. Methodology: In the present study, general scientific, as well as special methods and provisions of dialectics were used. In the course of the study, private scientific methods were also used including historical-legal, formal-legal, formal-logical, systemic, and comparative. Results: Currently, the new legal institution of agreement (mediation) is actively developing in the global legal system, contributing to resolving the conflict without holding a trial and just by holding peace negotiations and concluding an agreement with the accused. This institution was initially established in countries with the Anglo-Saxon legal system (USA, UK), and then was developed in countries with a continental legal system (RF, Moldavia, Kazakhstan). Implications/Applications: The mediation is considered to be a convenient approach for resolving conflicts, since it is built on the mutual agreement of two confrontational parties, and it will continue to further develop worldwide and will be included in the legislation of those countries where it has not been fixed yet.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".