Державне регулювання передвиборної кампанії у соціальних медіа (сучасна практика іноземних країн)
Bibliographic record
Abstract
Social media continues to increase its popularity of use and influence on the political landscape. At the same time, the legislative regulation of their application is an urgent problem for legal political science. This especially applies to election processes.Also, the review makes it possible to trace the next aspects at the state level: definitions of the concepts of social media or networks, identification of campaign content on the Internet, identification of political advertising and its labeling, etc.The conducted analysis showed that in Austria, Belgium, Denmark, Costa Rica, Mauritius, Monaco, Seychelles, Finland, Sweden, there are no separate laws or articles regulating campaigning on social media during the pre-election period. And in such countries as Honduras, Ecuador, Iceland, Canada, Cyprus, Latvia, the Netherlands, Germany, New Zealand, Slovakia, Hungary, France, Croatia, the Czech Republic, Chile, Montenegro there is no mention of where social media would be used for campaigning. However, it is considered that existing articles of current laws apply to such cases. So, in general, the studied countries (more than 30) regarding the regulation of pre-election campaigning in social media can be conditionally divided into three groups: 1) those that have relevant norms in laws or by-laws; 2) there is no legal regulation; 3) there is no mention of social media, but it is believed to be regulated by current state legislation.Further research may concern Ukraine and foreign countries, the legislation of which was not analyzed in this article, as well as the application of legal norms regarding campaigning in social media in practice. Similarly, in the future, it is appropriate to consider the various definitions of concepts ("online platform", "social media", etc.) in different countries, distinguishing common and different components during comparison.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.023 |
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".