The present study aimed to determine the dynamic relationship between good governance, fiscal policy, and economic growth in Oman. In the context of the current study, researchers chose a quantitative approach to answer the research questions, utilizing the latest 2023 data from the World Bank and The Global Economy databases. The data for the current study was carefully selected using variables that represent aspects of governance, fiscal policies, and economic performance. Our analysis uses Ordinary Least Squares (OLS) regression and the Autoregressive Distributed Lag (ARDL) Model. These methods help us understand these factors’ immediate and long-term impacts on Oman’s economy. The results we obtained offer fascinating insights into the country’s economic dynamics. We observe bidirectional causal relationships between the Good Governance Index (GGI) and the Regulatory Quality Index (RQI) and economic growth, while Fiscal Policy Effectiveness (FPE), Government Efficiency Index (GEI), and the Rule of Law Index (RLI) exhibit unidirectional causality towards GDP. Budget Balance (BB) shows no causal relationship with GDP, implying external factors influence it. Additionally, moderation analysis underscores the significance of digital financial inclusion in amplifying the effects of governance and fiscal policies on economic growth. These findings hold practical implications for policymakers and stakeholders in Oman. Specifically, they highlight the importance of governance, regulatory quality, and effective fiscal policies in shaping the economic landscape. To foster sustainable economic development, efforts should improve governance, enhance fiscal policy effectiveness, and promote digital financial inclusion.

Gender inequality is a structural social problem, associated with history, culture, education, religion and politics, this difficulty occurs in all social institutions due to the heterogeneity of the structure in the sexual division of labor, socioeconomic inequality, inclusion and inequity in participation in the public space between men and women. Public policies and attitudes towards gender equality in Peruvian university students were analyzed according to socio-academic variables. A descriptive-comparative study, with a quantitative approach, and not experimental cross-sectional, involved 776 university students from a public and a private university in Peru, intentionally selected. Adaptive attitudes (57.9%) were found to tend to be sexist; Likewise, in the study dimensions, the same trend was found in the sociocultural and relational levels, while in the personal dimension students develop sexist attitudes (62.4%). It is concluded, attitudes towards gender equality are sexist reproduction that is influenced by the sociocultural environment of the family, this situation occurs to a greater extent in men, while female students present attitudes of equality in greater intensity to seek equity in the distribution of roles.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.