Cold food chain logistics plays an important social role in maintaining the quality and safety of perishable products and ensuring the sustainability of supply chains. The purpose of the study is to identify the dynamics of development, thematic structure, and promising areas of scientific research in the field of logistics and management of the cold food chain. The research methodology is based on a systematic bibliographic, bibliometric, and substantive analysis of scientific publications. The initial dataset included 936 publications presented in the Dimensions database for 2002-2024; after thematic selection, 250 papers were selected for in-depth analysis. Data processing was carried out using VOSviewer and Biblioshiny programs and included an analysis of publication dynamics. The results showed that the average annual increase in the number of publications was 9-11%, with 38.6% of the papers published in 2021-2024. Authors from 32 countries participated in the research; China accounted for 25% of the publications, India and the USA – 6.5% each, Italy and the UK – 5% each. Ten leading sources provided about 39% of the publications presented in 58 scientific journals and collections. The subject structure was dominated by engineering sciences (20%), business and management (15%), computer science (13%) and decision sciences (10%). Seven main thematic areas are highlighted: temperature control, sustainable development, food safety, technological innovation, digitalization, socio-economic sustainability and infrastructure development. The results systematize the current state of research and can be used to develop digital, technological, and management solutions for the sustainable development of cold chains.

Youth tourism performs important social, educational, and wellness functions, but its development requires accessible, safe, and adapted infrastructure. The purpose of the study is to assess the current state of the infrastructure of children's and youth tourism in the Akmola region and determine the main directions of its socially oriented development. The study uses a systematic literature review, content analysis, statistical and comparative analysis. The initial information base consisted of official data from the Bureau of National Statistics of the Republic of Kazakhstan, the Department of Tourism and the Department of Education of the Akmola region for 2020-2025, regulatory acts and government programs, as well as 17 scientific publications selected from the Scopus and Google Scholar databases. The results showed that in 2020-2025, the number of tourist organizations in the region increased from 61 to 80 units, accommodation facilities from 374 to 458, children's health camps from 12 to 17, and tourist routes for children and adolescents from 18 to 36. The number of visitors served by the accommodation facilities increased from 235.0 to 659.4 thousand people, or by 180.6%. The share of children and teenagers in the total tourist flow increased from 21% to 31%. In 2025, the volume of services provided by the region's locations amounted to KZT 34,033.3 million, of which an estimated KZT 10,550.3 million were related to youth tourism. The results obtained confirm the need to develop specialized and inclusive facilities, year-round routes, educational programs, human resources, and public-private partnership mechanisms.

Contemporary societies are undergoing substantial changes in family formation and reproductive behaviour, accompanied by shifts in socially shared meanings of family, marriage, pregnancy, children, and parenthood. This study examined the structural organisation of five related social representations concerning family formation and parenthood among young adults in Russia. The method of free associations and the prototypical analysis of P. Verges were used. The answers were lexically and semantically standardized and combined into semantic categories. The empirical basis was based on the results of an online survey of 364 students and university students aged 17-25 living in Russia, including 218 women (59.9%) and 146 men (40.1%). Data collection was conducted from December 2025 to February 2026. Each participant named three associations for each of the five stimuli. Ideas about family and marriage had mostly positive content: 44.8% and 47.3% of respondents noted love, respectively. Pregnancy and parenthood with many children were more often associated with responsibility and stress: 14.6% and 31.3% indicated physical and emotional stress, respectively, and 24.7% of participants indicated financial stress with parenthood with many children. Social concepts of family, marriage, pregnancy, child, and parenthood share common semantic elements but differ in their structural positions. The results demonstrate the possibilities of a comparative structural approach to study how young people in Russia organize socially shared knowledge about starting a family and future parenthood.

The system of social guarantees for workers engaged in harmful and dangerous working conditions remains an important element of social protection. The purpose of the study is to assess the distributional fairness of the current system of social guarantees in Kazakhstan, compare it with foreign models and determine the possibilities of transition from a list–based to a risk-based mechanism for providing guarantees. The research methods include comparative institutional and legal analysis, targeted content analysis of legislation, descriptive statistical analysis, as well as an assessment of the financial and incentive effects of the proposed reform. The initial data cover the legislation of 11 countries — six CIS countries (Kazakhstan, Russia, Belarus, Uzbekistan, Kyrgyzstan, Armenia) and five OECD countries (USA, Germany, Great Britain, France, Canada), as well as data from the form of the Bureau of National Statistics of the Republic of Kazakhstan for 2021-2025. The results showed that the number of employees receiving at least one social guarantee remained virtually unchanged, while the share of those covered by guarantees decreased from 41.54% to 38.75% (-2.79 percentage points). Employers' expenses increased from 167.9 to 341.9 billion tenge, that is, by 2.04 times in nominal terms and by 26.4% in real terms the expression. The proposed transition to the differentiation of guarantees according to the measured risk level and the contribution scale of 3-9% allows maintaining fiscal neutrality and at the same time creating an economic incentive for the employer to reduce occupational risk.

Inefficient allocation of financial capital can increase the vulnerability of companies, limiting their financial stability and creating social consequences for stakeholders. The aim of this study is to examine how capital misallocation affects corporate financial distress among Chinese listed companies from 2010 to 2023 and to explore the underlying mechanisms of this relationship. The empirical analysis is based on panel data from Chinese companies whose shares are traded on the A-shares market for 2010-2023. The source data is obtained from the CSMAR database. Regression models with individual and temporary fixed effects and robust standard errors were used to evaluate the relationships. The results show that inefficient capital allocation significantly increases the risk of financial insolvency. The stability of the result is confirmed by using the O-score (0.0410), including the regional financial development indicator (-0.1189) and excluding observations for 2019-2020. An analysis of the mechanisms shows that inefficient capital allocation reduces the operating profitability of companies (-0.0094) and at the same time increases the degree of manipulation of financial leverage (0.0423). Inefficient capital allocation increases the risk of financial distress among nonlabor- intensive companies (-0.1379) and companies in competitive industries (-0.1196). The results show that improving the efficiency of financial resource allocation and limiting excessive debt burden can help reduce the risk of financial insolvency and strengthen the sustainability of companies.

The development of labour resources is one of the key factors in the socio‑economic development of regions, while significant territorial differences in demographic and labour characteristics necessitate their comprehensive assessment. The aim of the study is to assess the level of development of labour resources in the regions of Kazakhstan and to identify interregional differences in their formation and use based on an integrated approach. The information base of the study consists of official regional statistical data from the Republic of Kazakhstan for 2014–2024, including indicators of population size, migration, unemployment, youth unemployment, self‑employment, employment, the number of hired workers, the labour force, and the employment rate. The study uses Z‑standardization of indicators and calculates integral indices for three blocks: demographic development, the labour market and social stability, employment and labour activity. The results revealed persistent interregional differences that require the implementation of differentiated policies. The group with a high level of labor resource development includes the city of Almaty, with a total integral index value of 1.010606; the Turkestan Region, with 0.841212; the Almaty Region, with 0.823939; and the city of Astana, with 0.490303. The lowest values of the overall integral index were recorded in the Ulytau Region (–0.666667) and the North Kazakhstan Region (–0.544550). The obtained results confirm the significant territorial differentiation in the formation and use of labor resources in Kazakhstan and can be used in developing differentiated measures of social and regional policy in the labor market.

The rapid spread of generative artificial intelligence (AI) has raised the question of its impact on employment not only in developed but also in transition economies. The purpose of the article is to assess the social aspects of employment transformation in Kazakhstan in the context of the spread of artificial intelligence based on a comparison of employment dynamics and the level of its formalization in sectors with different exposure to AI in 2010-2024. The methodology includes an industry classification of AI exposure based on the international AI Industry Exposure index, a comparative analysis of employment dynamics, calculation of average annual growth rates, correlation analysis and intergroup statistical tests. The initial information base was made up of official data from the Bureau of National Statistics of the Republic of Kazakhstan on 17 types of economic activity for 2010- 2024. The results showed that in 2010-2024, the average employment growth was 58.5% in sectors with high exposure to AI versus 31.1% and 34.7% in sectors with medium and low exposure, respectively. After 2022, the average annual employment growth rate in highexposure sectors decreased from 3.58% to 1.98%, while in lowexposure sectors it increased from 1.33% to 2.28%. The relationship between the level of exposure to AI and the change in growth rates after 2022 turned out to be negative and statistically significant (r=- 0.53; p=0.028). The results obtained do not allow us to establish the causal effect of AI on employment, however, they record differences in industry dynamics after the beginning of the spread of generative AI and indicate the need for further analysis at the level of professions and work tasks.

The development of artificial intelligence and environmental innovations is changing the processes of knowledge management, decision‑making, and organizational learning in companies in the digital sector. The aim of the study is to assess the impact of implementing artificial intelligence and green innovations on organizational learning, taking into account the mediating role of decision‑making quality and knowledge management potential, as well as the moderating role of digital infrastructure in the information and communication technology sector in Pakistan. The study uses a quantitative approach and a cross‑sectional survey method. The initial data were obtained based on a questionnaire survey of 300 employees of companies in the information and communication sector in Lahore, Pakistan. The results demonstrated a positive impact of artificial intelligence implementation on organizational learning (β=0.650; t=5.520; p<0.001) and green innovations on organizational learning (β=0.400; t=3.921; p=0.002). The quality of decision‑making (β = 0.504; t = 4.134; p = 0.008) and knowledge management potential (β = 0.555; t = 5.428; p < 0.001) are also positively associated with organizational learning. Digital infrastructure strengthens the link between the implementation of artificial intelligence and organizational learning (β=0.220; t=2.45; p=0.031), as well as between green innovations and organizational learning (β=0.318; t=3.29; p=0.001). The results obtained are of social significance, as the development of artificial intelligence, green innovations, and digital infrastructure contributes to improving knowledge exchange, collaborative learning and the adaptability of employees in organizations.

The teacher labor market reflects not only the need of educational organizations for teaching staff but also the differences in wages, requirements for professional experience and competencies, which can create social inequality within the profession. The aim of the study is to identify the specific features of hiring teachers in Kazakhstan based on an analysis of wages, requirements for work experience, subject specialization, and professional competencies.The empirical basis of the study consisted of 17 997 vacancies selected from the initial set of 30 460 listings posted on the HeadHunter and Enbek platforms in January-February 2025. The work used methods of descriptive statistics and analysis of average and median salary values. The results showed that out of 17 997 vacancies, 13 510 were aimed at candidates without work experience; the median offered salary for them was 110 760 tenge, while for vacancies requiring up to three years of experience, it was 155 000 tenge. The highest median salary was recorded for English teachers — 250 000 tenge, whereas for primary school teachers, with 3 997 vacancies, it was 117 960 tenge. The results show that support at the beginning of a career, structured onboarding, and closer monitoring of subject‑specific pay differences are important for improving recruitment, professional advancement, and reducing inequality among teachers in Kazakhstan.