INTEGRATION OF LEGALTECH AND AI IN THE UKRAINIAN NOTARIAT: ENSURING SUSTAINABLE TURNOVER
Bibliographic record
Abstract
The article examines the process of integrating LegalTech and AI into the activities of the Ukrainian notariat in the context of digital transformation, war and post-war reconstruction. The author proves that the integration of LegalTech and AI into the activities of the Ukrainian notariat does not pose a threat to the traditional role of the notary, but, on the contrary, strengthens its function as a guarantor of legal certainty, authenticity and non-contentious justice. Based on a comparative analysis of the experience of Brazil, Mexico, Canada and Estonia, the author identifies models of electronic notaries that may be relevant to Ukrainian realities. The author also examines legislative initiatives in Ukraine, in particular the draft Law of Ukraine "On Amendments to Certain Legislative Acts of Ukraine on Improving the Regulation of Notarial Activities". The author emphasises the importance of ensuring cybersecurity, preserving notarial secrecy, regulatory definition of the boundaries of responsibility of notaries and technology providers, and the need to improve the digital literacy of notaries. The article emphasises that the Ukrainian experience is unique, since the digitalisation of the notariat sphere is taking place in the context of war, which requires a cautious and adaptive approach to the introduction of innovations to ensure sustainable economic circulation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".