Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.

According to the literature on innovation, several vital factors or determinants favor innovation in companies. In the case of R&D, significant advances have been made in the last two decades, which have enriched our understanding of its impact on various innovation outcomes. However, due to a lack of data availability, its study is difficult to address in emerging markets. This is why, using microdata from 5588 firms, we investigate the relationship between R&D investment and the impact on product and process innovations in different Latin American countries. We contribute to the literature by employing a machine learning methodology and comparing its results to a traditional innovation model. Our findings demonstrate the behavior of both product and process innovation methods.

Obesity and depression are public health issues of increasing concern worldwide. This study aims to evaluate programs that address obesity and their impact on depressive symptoms in children and adolescents. Obesity and depression share a bidirectional relationship, where each can serve as both a cause and a consequence of the other. Methods: A systematic review and meta-analysis were conducted following PRISMA criteria, with the registration recorded under PROSPERO code (CRD42024550644). The selected publications report on intervention programs for obesity and depression in children and adolescents aged 6 to 18 years. The selection was from databases including PUBMED, SCOPUS, LILACS, COCHRANE, WOS, SciELO, and ScienceDirect, using PICOS criteria to define inclusion. ROB-2 and ROBINS-1 were applied to assess bias. Results: Out of 3376 articles reviewed, eight met the inclusion criteria, some including several programs. These programs varied in duration and type, demonstrating changes in reducing Body Mass Index (BMI) and depressive symptoms. However, evidence supporting the effectiveness of programs that address both conditions is limited, particularly in developing countries. Additionally, the results exhibit high heterogeneity due to the diversity of evaluation criteria and methodological approaches, highlighting considerable risks of bias. Conclusions: Intervention programs for obesity management show statistically significant effects on depressive symptoms, although there is heterogeneity in the designs for their standardization and long-term follow-up strategies; however, the evaluations consider DSM-5 and ICD-11 criteria, which contributes to homogeneity. It is vital to address these closely related issues from a multidimensional perspective, considering socio-emotional and psychological factors, and to promote early intervention to maximize effectiveness and enhance quality of life at various stages of development.

(descriptivo): Las prácticas de crianza delimitan relaciones de influencia que pueden representar un riesgo para el desarrollo de conductas problemáticas en los infantes. Se realizó una investigación que correlacionó las prácticas de crianza de familias en una comunidad vulnerable de la ciudad de Barranquilla, con los mecanismos de desconexión moral que emplean sus hijos al afrontar conflictos. Se encontró un nivel favorable en las prácticas implementadas por las familias y una baja tendencia de los niños hacia el uso de la violencia. Se encontraron correlaciones positivas y negativas entre algunas prácticas de crianza y casos donde los niños se desconectaron moralmente. Cuando los padres instan a usar la violencia en el manejo de las relaciones interpersonales, los niños emplean formas menos empáticas de desconexión moral, como la deshumanización y la culpabilización de la víctima.