Bibliographic record
Abstract
в начале 2000 года, сложно было представить, что через 24 года у трёх из четырёх жителей России будет доступ к интернету. А пользоваться социальными медиа будут 73% населения. Такие данные приводит Datareportal в отчёте отчете «Digital 2024: Country Headlines Report» [Datareportal]. Подобная статистка говорит о том, что уровень цифровизации почти за одну четверть века текущего столетия значительно вырос. Чем больше новых цифровых платформ, систем и процессов, тем больше появляется новых цифровых инструментов. Одним новым, ещё малоисследованным инструментом служит термин – дипфейк. Целью настоящего исследования является теоретическое и сущностное изучение понятия дипфейка. Нового инструмента, с помощью которого возможно повлиять на политическую коммуникацию. Задачами исследования являются: изучить и раскрыть понятие дипфейка и его разновидностей, определить кто и каким образом может использовать дипфейки в политической коммуникации, оценить риски и угрозы дифейков, и предложить пути решения данной цифровой угрозой. Результаты. В ходе работы были выявлены сущность и основновные виды дипфейков, приведены примеры политических угроз с применением дипфейков, сделан вывод и дано предложение о необходимости разработки нормативно правовой базы, устанавливающей и регламентирующей работу создание дипфейков. вack in the early 2000s, it was difficult to imagine that within 24 years, three out of four Russians would have access to the internet. And 73% of the population would use social media. These statistics are provided by Datareportal in its report "Digital 2024: Country Headlines Report". Such figures indicate that the level of digitization has increased significantly over the last quarter of the current century. With the emergence of new digital platforms, systems, and processes, new digital tools have also emerged. One such new tool, which is still relatively unexplored, is deepfake. The purpose of this study is to theoretically and essentially study the concept of deepfake, a new tool that can be used to influence political communication. The objectives of the research are: to study and reveal the concept of deepfake and its varieties, to determine who can use deepfakes in political communication and how, to assess the risks and threats of deepfakes, and to propose ways to solve this digital threat. Results. In the course of the work, the essence and main types of deepfakes were identified. Examples of political threats using deepfakes are given, a conclusion is drawn and a proposal is made on the need to develop a regulatory framework that establishes and regulates the creation of deepfakes.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".