Some Aspects of the Integration of Natural and Legal Knowledge and Issues of Judicial Control (Past and Present)
Bibliographic record
Abstract
Crime expansion and its structural changes created a need for new general approaches to developing methods of discovering and analyzing crimes characterized by high latency. Theoretically, facts of crimes can be discovered at the objects of management and financial activities with the help of traditional criminalist and investigation methods. However, for improving the cited above methods it became of great need to establish scientific interconnections. That’s why economic and criminalist analysis is widely used as the former is based on studying body of technical and financial indices of objects under control under investigating crime signs, which usually manifest themselves as breaking stable interconnections between indices. Along with advantages of the cited above approach the former is characterized by laboriousness and high requirements to inspection activity DOI: 10.32370/IA_2022_06_4 including well-qualified auditors, time and funds spending. Just according to the cited above circumstances the researchers have developed scientific concept of criminalist and mathematical approach to analyzing and detecting crimes. The problem of society and individual’s interests’ correlation in increasing fighting organized crime is one of the most complicated. Every state solves it considering existed legal traditions, ideas, legal conscience of the people, and even their mentality. But the general tendency is that the means of fighting organized crime are more severe than those applied for fighting not organized crime. Papers of UNO X Congress on Preventing Crime and Treatment of Law Violators (Vienna, April 2000) prove that because of increasing scale and appearing new forms of transnational organized criminality, it is worth reconsidering some traditional opinions concerning justice for law violators, and some their rights as they are an obstacle for effective investigation and prosecution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".