This study presents an advanced hybrid approach for detecting near-duplicate texts in the Kazakh language, addressing the specific challenges posed by its agglutinative morphology. The proposed method combines statistical and semantic techniques, including N-gram analysis, TF-IDF, LSH, LSA, and LDA, and is benchmarked against the bert-base-multilingual-cased model. Experiments were conducted on the purpose-built Arailym-aitu/KazakhTextDuplicates corpus, which contains over 25,000 manually modified text fragments using typical techniques, such as paraphrasing, word order changes, synonym substitution, and morphological transformations. The results show that the hybrid model achieves a precision of 1.00, a recall of 0.73, and an F1-score of 0.84, significantly outperforming traditional N-gram and TF-IDF approaches and demonstrating comparable accuracy to the BERT model while requiring substantially lower computational resources. The hybrid model proved highly effective in detecting various types of near-duplicate texts, including paraphrased and structurally modified content, making it suitable for practical applications in academic integrity verification, plagiarism detection, and intelligent text analysis. Moreover, this study highlights the potential of lightweight hybrid architectures as a practical alternative to large transformer-based models, particularly for languages with limited annotated corpora and linguistic resources. It lays the foundation for future research in cross-lingual duplicate detection and deep model adaptation for the Kazakh language.

This article explores contemporary models of the development of methodological competence, focusing on the needs of IT discipline teachers. The challenges in implementing these features within modern educational conditions are identified, underscoring the necessity for creating innovative teaching methods adapted to the requirements of IT teachers. By analyzing current trends in the educational environment, the authors highlight key stages in the continuous education system for teachers, including the mastering of basic education, adapting young teachers, and fostering their professional development. The article reveals the actual possibilities of developing the methodological competence of teachers as an ongoing endeavor to elevate their professional pedagogical culture. In the article, the authors propose a conceptual model within the domain of education, serving as the basis for constructing an efficient mathematical model which is specifically designed to create individualized learning trajectories for IT discipline teachers with the focus on managing the process of methodological competence development during the synthesis of training courses. The authors propose an innovative approach to teacher retraining, centered around individualized needs and abilities, with the aim of enhancing the quality of education in the field of information technology.

This article considers the problem of assessing the recent publication productivity of scientists based on PageRank class methods and proposes to use these assessments to solve the problem of selecting scientific partners for R&D projects. The methods of PageRank, Time-Weighted PageRank, and the Time-Weighted PageRank method with Citation Intensity (TWPR-CI) were used as a basis for calculating the publication productivity of individual subjects or scientists. For verification, we used the Citation Network Dataset (Ver. 14) of more than 5 million STEM publications with 36 million citations. The dataset is based on data from ACM, DBLP, and Microsoft Academic Graph databases. Only those individual subjects who published at least two articles after 2000, with at least one of these articles cited at least once before 2023 year, were analyzed. Thus, the number of individual subjects was reduced to 1,042,122, and the number of scientific publications was reduced to 2,422,326. For each of the methods, a range of estimates of productivity is indicated, which are obtained as a result and possible options for making decisions on the selection of potential individual subjects as performers of R&D projects. One of the key advantages of the TWPR-CI method is that it gives priority to those researchers who have recently published and been cited frequently in their respective research areas. This ensures that the best potential R&D project executors are selected, which should minimize the impact of subjective factors on this choice. We believe that the proposed concept for selecting potential R&D project partners could help to reduce the risks associated with these projects and facilitate the involvement of the most suitable specialists in the relevant area of knowledge.