This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

The main objective of the study is to discuss the application of a participatory approach that involves the community of a small rural area in Italy to develop and maintain a sustainable local food system based on a very ancient and high-quality typical local bean. The efficacy of the approach in terms of the active involvement of local actors (farming communities, local administration, social associations, and civil society) and knowledge transfer for preserving the local food culture has been demonstrated. Possible improvements to the approach through digital technologies for stimulating the effective engagement of teenagers have also been discussed.

This study investigates the impacts of converting agricultural land into agrotourism areas on environmental, socio-cultural, and economic perspectives within Batukliang District, Central Lombok Regency, Indonesia. With a case study approach, this qualitative descriptive research employed interviews with three target groups: local farmers, residents, and tourism actors. The findings revealed seven key points identified as influences affecting the socio-cultural aspects of land change, including community impact, cultural preservation, cultural identity loss, community dynamics change, local cultural commercialization, cultural heritage loss, and traditional livelihoods. The results also unveiled nine financial impacts, 8 of which were associated with economic implications such as economic challenges, risk management, brand building, costs and investments, market access, increased revenue, and income diversity, which contribute positively to local economic development. The study concluded that integrating community involvement empowerment strategies, income diversification, sustainable farming promotion, and land-use regulation is crucial for developing a successful sustainable agrotourism destination.

Research on community resilience has been ongoing for decades. Several studies have been carried out on resilience in different groups and contexts. However, few address the relationship between community resilience and depopulated rural areas. This study aims to dig deeper into this, considering the concrete impact of population decline in Spain. We carried out a systematic review of the most relevant contributions. A search protocol was developed and used to consult ten databases. Different combinations of terms such as ‘community resilience’, ‘rural’, and ‘depopulation’, or related terms, were used. 22 scientific texts were analysed. We obtained a set of publications that demonstrate the heterogeneity of research methods, approaches and analytical processes applied to the study of this relationship. A mostly qualitative approach was observed, either as the main technique or complementary to documentary reviews. The results underscore the complex nature of rural depopulation and related constructs. It emphasizes the specific importance of community resilience in these territories in terms of social capital, endogenous resources, sustainability, economic dynamism, local responsibility and effective governance. The findings identify a scarce mention to social intervention professions, which should have a more important role due to their core values. In the studies reviewed, it appears as an emerging and scientifically relevant area to explore, both for investigation and intervention purposes. The strength of a multidisciplinary approach to addressing the phenomena appears in the discussion as a main potential line of research.

The purpose of this study was to assess rural students’ computational thinking abilities. The following proofs were observed: (1) Students’ abstraction affected algorithmic thinking skills; (2) Students’ decomposition influenced algorithmic thinking skills; (3) Students’ abstraction impacted evaluation skills; (4) Students’ algorithmic thinking affected evaluation skills; (5) Students’ abstraction impacted generalization skills; (6) Students’ decomposition impacted generalization skills; (7) Students’ evaluation affected generalization skills. Gender differences were observed in the relationship among the computational thinking factors of junior high school students. This included the abstraction-generalization skills; evaluation-generalization skills; and decomposition-generalization skills relationships, which were moderated by the gender of the students. 258 valid surveys were collected, and they were utilized in the study. Conducting the descriptive, reliability, and validity analyses used SPSS software, and the structural equation modeling (SEM) was also conducted through Smart PLS software to assess the hypothetical relationships. There were gender disparities in the correlation among computational thinking components of the junior high school students’ studying in rural areas. Research has shown that male and female students may have different abstractions, evaluations, and generalizations related to computational thinking, with females being more strongly associated than males in non-programming learning contexts. These results are expected to provide relevant information in subsequent analyses and implement a computational thinking curriculum to overcome the still-existing gender gaps and promote computational thinking skills.