Modeling the Performance of Criminal Law Functions in the Context of Safety and Security Development
Bibliographic record
Abstract
The main purpose of the article is to ensure the safety and security development of the country through the effective implementation of its law functions.For this, the main scientific task is to model the correct execution of criminal law functions in the context of safety and security development (provinces and territories of Canada).The object of the study is the system for ensuring safety and security development.The research methodology involves the use of modern modeling methods.IDEF, SMART, and SWOT methods are used.As a result, we received an information diagram of the performance of criminal law functions in the context of safety and security development.The elements of the novelty of the results of the study are presented by presenting the main stages of the implementation of criminal law functions in the context of safety and security development.The information diagram consists of several blocks that, through arrows, allow you to explain key processes.The innovativeness of the article is presented in the form of a model for the implementation of criminal law functions in the context of safety and security development.The study is limited by taking into account only criminal law functions.One of the limitations is not only the functions, but also the narrowing to the criminal law of only one country, and, as a result, the specifics of the law of other countries are not taken into account.Prospects for further research should be devoted to aspects of ensuring legal security in the framework of safety and security development planning.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".