Abstract:

The paper addresses the problem of constructing interpretable vector representations of scientific texts for intellectual academic genealogy. A typology of embeddings is proposed, comprising three classes: statistical, learned neural, and structured symbolic. The study argues for combining the strengths of neural embeddings (high semantic accuracy) with those of symbolic embeddings (interpretable dimensions). To operationalize this hybrid approach, an algorithm for learned symbolic embeddings is introduced, which utilizes a regression-based mapping from a model’s internal representation to an interpretable vector of scores.

The approach is evaluated on a corpus of fragments from dissertation abstracts in pedagogy. A compact transformer encoder with a regression head was trained to reproduce topic relevance scores produced by a state-of-the-art generative language model. A comparison of six training setups (three regression-head architectures and two encoder settings) shows that fine-tuning the upper encoder layers is the primary driver of quality improvements. The best configuration achieves R² = 0.57 and a Top-3 accuracy of 74% in identifying the most relevant concepts. These results suggest that, for tasks requiring formalized output representations, a compact encoder with a regression head can approximate a generative model’s behavior at substantially lower computational cost. More broadly, the further development of algorithms for constructing learned symbolic embeddings contributes to building a model of formal knowledge representation in which the convergence of neural and symbolic methods ensures both the scalability of scientific text processing and the interpretability of vector representations that encode their content.

Abstract:

The List of peer-reviewed scientific journals of the Higher Attestation Commission is gradually turning into a complex information system based on the normative documents of the Higher Attestation Commission, bibliometric data eLibrary.ru, decisions of the expert councils of the Higher Attestation Commission and working groups engaged in the analysis, ranking and categorization of the journals of the List. The Russian Scientific Journals (RSJ) database created by RIEP can become a system that serves the requests of different categories of users related to the topic of dissertation defense. So far, the RSJ has implemented the interface of a representative of the editorial board of the journal and the interface of a member of the expert council of the Higher Attestation Commission. It is desirable to include in the RSJ an open interface addressed to the degree applicant to verify compliance with the requirements of the Higher Attestation Commission for publications in journals from the List. With the established mutual exchange of data between RSJ and eLibrary.ru, the applicant's interface with the designated functionality can be organized in the eLibrary.ru user environment.

Abstract:

The impact of the COVID-19 pandemic on the world of scientific publications: rapid publication and simplified access for articles about the virus, open access for a time of self-isolation in the world, and in Russia. There is an impressive difference between the number of readers for Russian scientific articles in open access and in paid access. The policy of journals of the Russian Academy of Sciences. Impressive growth in readership of CyberLeninka. Online meeting of the dissertation council. eLibrary news. Comprehensive publication activity score from the Ministry of education and science.

Abstract: The article considers conceptual bases of creation on the basis of leading universities of the Russian Federation expert-analytical centers to conduct an independent examination of scientific texts (theses, monographs, articles, etc.) in the presence of incorrect borrowings. The basic stages of work of centres for the validation of scientific texts, the project forms an expert opinion.

Abstract:

Digital resources of the Russian State Library (RSL) are composed of the Digital Library including 11 collections, the largest of which and the one of the greatest demand is the Digital Dissertation Library, of the RSL Internet Store and of other electronic services. The Russian State Library is in a constant process of broadening its electronic collections, and it would develop the very system of its remote services for readers, too. Thus the RSL makes its contribution to the quality of science and education in Russia, to cultural education of the nation and to creation of a united library and information environment within the state.

Abstract:

In accordance with the requirement of the Higher Attestation Commission (HAC), journal issue data from the List of Peer-reviewed scientific publications in which the main scientific results of dissertations for the degree of Candidate of Sciences and for the degree of Doctor of Sciences (HAC List) have been regularly published in the Russian Science Citation Index (RSCI) in the bibliographic database eLibrary.ru for more than 20 years. In March 2023, the editorial offices of journals from the HAC List, in accordance with the recommendation of the HAC, have post data of 2022 year issues in the Russian Scientific Journals database (RSJ) created by the Russian Scientific Research Institute RIEPP. In April 2025, by order of the Ministry of Science and Higher Education of the Russian Federation, a new requirement was added — for a journal from the HAC List, along with registration in the RISC eLibrary.ru, registration in the Information System (IS) “Metaphora”, developed by the Russian Center for Scientific Information, is required. Journals from the HAC List are recommended to regularly transfer metadata of published issues of journals to the “Metaphora” through specially organized interfaces. What role do the RSJ and “Metaphora” databases play in the infrastructure of scientific publications?

In addition, according to commission of the Government of the Russian Federation, the Russian Center for Scientific Information performs the function of the operator of the “White List” of scientific journals. The “White List” in 2023 was formed by the Interdepartmental Working Group (IWG) of the Ministry of Education and Science of the Russian Federation. The "White List" is supposed to be used to monitor and evaluate the publication activity of Russian scientists. The "White List" currently includes about 29,000 English-language international journals and about 1,000 Russian-language journals from the Russian Science Citation Index (RSCI) database. In 2025, the Russian-language part of the "White List" significantly expanded due to the inclusion of journals from HAC List into the "White List". We would like to receive detailed information from the ideologists of the "White List" on how the levels (U1, U2, U3, U4) of the “White List” journals and the categories (K1, K2, K3) of journals on the HAC List will correspond?