Принцип тайного голосования при использовании современных информационных технологий
Bibliographic record
Abstract
Election is the most important tool in democratic decision making. The provision of secrecy of the ballot is the biggest problem of electronic voting. The research goal is a detailed consideration of the principle of secret suffrage of electronic voting, research of rules of secret voting. The general research methods used were as follows: comparative legal research, induction and deduction, synthesis and analysis, dialectical research. The article considers the international legislation in tha area of secret suffrage (for example, Code of good practice in electoral matters). Moreover, the possible violations of secret voting are analyzed. There is a review of recent achievements in the area of defense of secret suffrage in foreign countries (Switzerland, Canada). The author proposes the protective means for secret voter’s will provision. The article concludes that further development of e-voting systems is imperative, as is the elaboration and the improvement of protective mechanism of secret suffrage.
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 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 | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.025 | 0.050 |
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; both teacher heads agree on what is shown here.
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".