The Main Systemic Engineering Problems of Using Computer and Digital Technologies in Legal Activities in the Context of Ensuring Security
Bibliographic record
Abstract
The relevance of the research topic is accompanied by a great demand for digital and computer technologies and their rapid growth in the activities of any organization. Legal activity and security have changed a lot in recent years and also feels the impact of modern digital and computer technologies. The main purpose of the article is to study the main systemic engineering problems of using digital and computer technologies in the legal activities of firms in terms of ensuring security. To achieve this goal, we used the methodology of hierarchical ordering using information and mathematical tools of the theory of graphs and relationships, which allows you to streamline and form a connection between the main systemic engineering problems of using digital and computer technologies in the legal activities of firms. Based on the results of the analysis, we have formed an information model of the hierarchical ordering of the influence of the main systemic engineering problems of using digital and computer technologies in the legal activities of firms in terms of ensuring information security. Our study has a number of limitations, and they are related to the inability to cover all types and types of problems of using digital and computer technologies in legal activities due to a large amount of data and limited work. Further research will require the question of analyzing the impact of Industry 4.0, which is already practically here and with us, in the legal activities of firms.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".