On digital transformation of justice and prospects for the economic sector
Bibliographic record
Abstract
The article focuses on analyzing the impact of digital transformation and technology on the judicial system. It highlights a terminological foundation that includes concepts such as digitization, digitalization, and digital transformation, which cover the transition of information into digital formats, the use of new tools for optimizing judicial processes, and comprehensive changes within the justice system. It establishes that digitalization involves the integration of technology at the level of automating judicial services, such as electronic document submission and electronic court hearings, whereas digital transformation encompasses the restructuring of justice itself. The importance of digital transformation is emphasized as a comprehensive reorganization of the entire judicial system through the adoption of advanced technology, laying the groundwork for electronic justice and enhancing access to justice. The conclusion drawn is that the development of the judiciary in Ukraine can proceed not only through the digitalization of judicial processes but also through the creation of new models for judicial processes, including the use of online courts. The online court model aims at an innovative transformation of the judicial environment, where digital technologies serve not merely as tools for automation but as drivers of a paradigm shift in delivering judicial services and resolving disputes. The article analyzes international experience in this area, specifically the operation of online courts in countries such as the United Kingdom and Canada for handling simple cases, reducing the burden on the judicial system, and simplifying procedures for citizens. The appropriateness of using online courts for resolving minor disputes and straightforward economic cases is substantiated. The role of artificial intelligence is considered separately, providing new opportunities to support judges in routine tasks, while also raising ethical and security risks, such as algorithmic bias and privacy threats. The argument is made that for the successful implementation of digital innovations in the justice system, it is necessary to develop infrastructure, establish standards for the ethical use of artificial intelligence, and ensure high levels of cybersecurity to protect the confidentiality of judicial information.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".