ҚИЛМИШНИ КВАЛИФИКАЦИЯ ҚИЛИШДА ЖИНОЯТ ТАРКИБИ ЗАРУРИЙ ВА ФАКУЛЬТАТИВ БЕЛГИЛАРИНИНГ ЎРНИ: ТАҲЛИЛ ВА ТАКЛИФ
Bibliographic record
Abstract
In this article, such research methods induction and deduction were widely used. In particular, it was noted about the history of the first appearance of the concept of corpus delicti, the absence of such a legal category in many countries of the world, including in the Anglo-Saxon legal system, replacing it with such terms as criminal act andcrime. Then the definition of the concept of corpus delicti was given, the opinions of scientists about this concept in the theory of criminal law, after which the elements of the corpus delicti and their compulsory and facultative signs were consistently described. Thus, the article reveals the place of compulsory and facultative signs of the crime in the qualification of acts, in particular, their criminal-legal aspects, problems of compulsory and facultative signs a part of some crimes in the Criminal code of the Republic of Uzbekistan. At the same time proposals and recommendations on improving the criminal legislation and the resolution of the Plenum of the Supreme Court of the Republic of Uzbekistan were given. Proposals and recommendations based on an analysis of the criminal law of Canada, France, Germany, Estonia, Russia, Belarus, Armenia, Kazakhstan, Kyrgyzstan and Tajikistan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".