MODERNIZATION OF THE ELECTORAL PROCESS IN THE CONDITIONS OF DEMOCRATIC TRANSFORMATIONS IN UKRAINE: A CRIMINALIST’S VIEW
Bibliographic record
Abstract
Shablystyi V. V. Modernization of the electoral process in the conditions of democratic transformations in Ukraine: a criminalist's viewThe article is devoted to the study of the modernization of the electoral process in Ukraine, with a focus on the implementation of e-democracy and digital technologies in the context of the country's democratic development.Based on the provisions of the Constitution of Ukraine, particularly Article 71, which guarantees free, equal, direct, and secret elections, the article analyzes key principles of democratic expression of will.The voting process is considered as a central stage of the electoral procedure, its importance in forming government bodies, and its vulnerability to corruption risks.Special attention is given to the Concept for the Development of E-Democracy in Ukraine, approved in 2017, which aims to engage citizens in socio-political processes through ICT, ensure government transparency, and improve the effectiveness of statepublic interaction.The article explores the concept of e-democracy in both narrow (using ICT for digital realization of citizens' rights) and broad (public involvement in solving societal issues through digital tools) senses.The study also examines global experiences in implementing electronic voting (Estonia, Australia, Switzerland, Canada, etc.), where digital technologies such as blockchain, biometric identification, artificial intelligence, and big data analysis contribute to increased transparency, security, and accessibility of elections.At the same time, challenges such as cyber threats, the complexity of ensuring vote secrecy, legal restrictions during martial law in Ukraine, and the low level of public trust in new technologies are emphasized.The article highlights the need for a comprehensive approach to the implementation of electronic voting, including the development of a reliable legal framework, technical infrastructure, cybersecurity mechanisms, and a broad information campaign to overcome public mistrust.It also addresses the need to adapt electoral mechanisms to the conditions of wartime and the post-war period, particularly to ensure the participation of Ukrainians abroad.In conclusion, it is emphasized that a successful digital transformation of the electoral process will strengthen democratic institutions, but requires a gradual implementation that takes into account the national context and international experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".