A study of popular science discourse in China and Russia using neural networks as informants

The subject of the research is the peculiarities of the popularization of scientific knowledge in China and Russia. The novelty of the study lies in the fact that for the first time nine artificial intelligence models developed in China, Russia and the USA were used as informants in the application of the survey method. Each of them was asked three questions about the trends in the popularization of scientific knowledge in China and Russia. The results showed that when describing China, the emphasis is on the role of the state and a top-down strategy, while in relation to Russia, the diversity of popularization institutions is emphasized. The main barriers are the insufficient level of scientific literacy and the spread of pseudoscience. With regard to cooperation between countries in popularizing science, AI models predict that artificial intelligence, biomedicine and public health, as well as ecology and energy, will become promising areas. It is noted that the responses of the models, despite their general consistency, contain inaccuracies and biases. The article argues that AI models can be used as a heuristic tool in scientific communication research, but in order to achieve accurate results, it is necessary to combine their findings with traditional empirical research. The research methodology is based on a survey of nine language models of artificial intelligence, who were asked three questions about the popularization of science in China and Russia, and all the texts received were analyzed. The novelty of the research lies in the use of neural network models as informants for studying the popularization of science. This allowed us to identify stable patterns: in the Chinese context, state policy and centralized management dominate, while in the Russian context, institutional fragmentation and regional differences prevail. Among the barriers are low scientific literacy, lack of trust, and the spread of pseudoscience. At the same time, AI models have pointed to promising areas of cooperation between China and Russia, including artificial intelligence, biomedicine, healthcare, ecology and energy. At the same time, limitations have been identified: the model responses contain inaccuracies, bias, and the risk of reproducing pseudoscientific judgments. In conclusion, it is emphasized that AI can serve as a heuristic tool for identifying discursive trends, but conclusions should be combined with traditional empirical methods to ensure verification and reliability of analysis.

scientific communication, popularization of scientific knowledge, artificial intelligence, large language models, neural networks, cross-cultural comparison, differences in popularization, public policy, barriers to popularization, cooperation between countries