## 1 Introduction
This helps educational institutions and employers to build trust with candidates and conduct fair employee certification. In thecontext of education globalization, such systems contribute to uniform assessment standards, making educational programs more comparable at an international level. As a result, workforce mobility increases, and diplomas and certifications obtained in different countries become more interchangeable and recognized, facilitating the employment of graduates in the global labor market. Kazakh (ISO 639-3, kaz) is a Kipchak (Northwestern) Turkic language with approximately ten million speakers [7; 8]. While the majority of Kazakh speakers live in the Republic of Kazakhstan, significant Kazakh-speaking populations exist throughout Central Asia. Furthermore, it should be noted that text modeling and near-duplicate detection in the Kazakh language have several distinctive features related to its morphological, syntactic, and lexical characteristics. Specifically, Kazakh has agglutinative morphology, meaning that words can be formed by attaching numerous affixes to the root. This results in many word forms and challenges in tokenization, as standard text segmentation methods may be ineffective. In Kazakh, grammatical relationships are determined by affixes rather than word order, which complicates models based on word sequences (e.g., N-grams). Additionally, lemmatization in Kazakh is challenging because standard algorithms may fail to account for numerous suffixes. Stemming is also complicated, as it requires correctly distinguishing between the root word and all possible affixes. Kazakh exhibits strong polysemy and synonyms, meaning that words can have multiple meanings depending on the context, and many concepts can be expressed using different words, making text classification and near-duplicate detection more difficult. The official and scientific styles in Kazakh often contain long and complex sentences, requiring specialized approaches to syntactic analysis and complicating text normalization. Moreover, an additional challenge is that Kazakh is a low-resource language. All these factors must be considered when developing and implementing hybrid methods for near-duplicate detection in text documents. The development of such methods for low-resource agglutinative languages has significant theoretical importance for the advancement of applied and corpus linguistics. Additionally, the creation and implementation of these methods have substantial practical significance, particularly for the academic community in Kazakhstan. The experience gained from implementing hybrid near-duplicate detection methods is valuable not only for Kazakh linguistics but also for other Turkic languages (e.g., Kyrgyz, Turkish, Azerbaijan, Uzbek), Mongolic languages, Uralic languages (e.g., Finnish, Estonian, Hungarian), as well as Korean and Japanese, which feature agglutinative morphology and complex affix/postfix systems. Thus, the development of combined models for text data analysis opens up new opportunities for enhancing academic integrity and preventing plagiarism. It is forecasted that the implementation of such methods will improve verification reliability by 30% and reduce cases of unintentional borrowing in academic articles and dissertations by more than 40% [9]. Thus, the development of hybrid methods for identifying near duplicates, considering the characteristics of agglutinative and low-resource languages, including Kazakh, is highly relevant.