March 31 · Article · «МЕМЛЕКЕТТІК АУДИТ – ГОСУДАРСТВЕННЫЙ АУДИТ» · Political Science and International Relations
This article examines the advancement of citizen-centric public service delivery through a comparative analysis of selected European Union (EU) countries and assesses the relevance of their experience for Kazakhstan. Against the background of global digital transformation and persistent digital inequalities, the study conceptualizes citizen-centricity not merely as a service design principle, but as an integrated governance capability. The research applies a qualitative comparative methodology and analyzes four EU member states: Estonia, Germany, the Netherlands, and Italy, representing different administrative traditions and models of digital public infrastructure. The analysis is structured around four interrelated dimensions: (1) the normative dimension, which frames citizen-centricity as a public value linked to legitimacy, trust, and equity; (2) the organizational dimension, focusing on service integration, administrative reengineering, and performance management aligned with citizen outcomes; (3) the data and technology dimension, addressing data governance, interoperability, and the use of advanced technologies such as artificial intelligence, big data, and cloud computing; and (4) the experiential dimension, capturing citizens’ lived experiences, including administrative burden reduction and perceived benefits of digital services. The results show that successful EU practices demonstrate coherence across all four dimensions, while fragmented or technology-driven approaches yield limited citizen-centric outcomes. For Kazakhstan, the findings suggest that further progress in digital public services requires stronger value-based governance, deeper inter-agency integration, responsible use of data and AI, and inclusive service design. The article concludes by outlining policy-relevant directions and identifying areas for further empirical research.
AAAigerim AmirovaOpen access
March 31 · Article · Eurasian Mathematical Journal · Statistics and Probability
We present a new method for dimension reduction that combines unsupervised dimension reduction (UDR) with sufficient dimension reduction (SDR). In unsupervised dimension reduction the goal is to find a low-dimensional linear subspace that approximates the support of a data distribution. If data is supervised, then in sufficient dimension reduction the goal is to find a low-dimensional linear subspace, called the effective subspace, such that the projection of an input vector onto that subspace maximally captures information on correlations between an input and an output.The objective that we suggest to minimize consists of two parts. The first one is responsible for the UDR part, it forces a low-dimensional probabilistic measure \(\mu\) to approximate a distribution over inputs. The second one is responsible for the SDR part, it forces a regression function \(f\) to be consistent with supervised data. Additionally, we require the support of \(\mu\) and the effective subspace of \(f\) to be equal. In this hybrid setting we solve two problems, UDR and SDR, so that the UDR term serves as a regularizer of the SDR term.We reformulate the problem as an optimization task of finding a \(k\)-dimensional linear subspace \(S\) and a pair of complex measures \((\mu,\mu')\) supported in \(S\). Instead of optimizing over complex measures, we suggest minimizing over ordinary functions \((g_1,g_2)\) but with an additional term \(R\) that penalizes a distortion of the common support of \(g_1,g_2\) from a \(k\)-dimensional linear subspace. The algorithm that we develop can be formulated for functions \((g_1,g_2)\) as well as for their inverse Fourier transforms. Eventually, we report results of numerical experiments on well-known datasets.
Rustem TakhanovOpen access
March 31 · Article · EduStudies · Language and Linguistics
This paper aims to explore the specific features and challenges of learning English in a non-linguistic faculty, with a particular focus on the experience of first-year students. The study examines students’ motivation, learning strategies, and attitudes towards English as a compulsory subject rather than a major field of academic specialization. Special attention is paid to how students perceive the relevance of English to their future professional careers and how this perception influences their level of engagement and academic performance. The paper also discusses the role of the academic environment, teaching methods, and students’ technical specializations in shaping their language learning process. A small-scale survey and classroom observation were conducted among first-year students studying English in a non-linguistic faculty in order to identify common difficulties, learning needs, and effective instructional practices. The collected data made it possible to determine key factors that affect students’ progress, including time constraints, varying levels of prior knowledge, and limited opportunities for practical language use. Based on the findings, the paper offers practical recommendations aimed at improving the effectiveness of English language instruction in non-linguistic higher education institution
MTMadina TussupbekovaOpen access
