Використання ІТ технологій в процесі підвищення кваліфікації адвоката
Bibliographic record
Abstract
This article reveals the legal nature of a lawyer's professional duty to improve his qualifications through the prism of the use of information technologies in the process of improving such qualifications. The position is argued, according to which the acceleration of the pace of the use of information technologies in the context of the professional activity of a lawyer, including in matters related to the improvement of his qualifications, is in a certain way determined by the requirements of today, taking into account the COVID-19 pandemic in the world in recent years, as well as the military Russia's aggression against the Ukrainian people. Within the framework of this article, the experience of leading foreign countries (in particular, the USA, Great Britain, Japan, Canada) was disclosed, primarily regarding professional development through distance education of lawyers. Emphasis is placed on the positive aspects of the reform of the Institute for the Advancement of Lawyers, as a result of which the Internet platform of the Accreditation Center of the Higher School of Advocacy of the National Bar Association of Ukraine was first created, which includes information on all accredited current and ongoing measures for the advancement of attorneys’ qualifications, which significantly simplifies access to information on their implementation. It is concluded that, on the one hand, the development by the Higher School of Advocacy of NAAU of special, adaptation courses (instructions) of professional development online for interns, assistant attorneys and persons, in the case of renewal of their right to engage in advocacy, is positive, and on the other hand, taking into account the fact that professional development is a professional obligation that applies to all Ukrainian lawyers, who are not exempted from the obligation to pay contributions precisely to ensure the implementation of lawyer self-governance, and therefore the passage of relevant measures (programs) developed by this institution must take place on a free basis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".