This research offers a solution to a highly recognized and controversial problem within the composite indicator literature: sub-indicators weighting. The research proposes a novel hybrid weighting method that maximizes the discriminating power of the composite indicator with objectively defined weights. It considers the experts’ uncertainty concerning the conceptual importance of sub-indicators in the multidimensional phenomenon, setting maximum and minimum weights (constraints) in the optimization function. The hybrid weighting scheme, known as the SAW-Max-Entropy method, avoids attributing weights that are incompatible with the multidimensional phenomenon’s theoretical framework. At the same time, it reduces the influence of assessment errors and judgment biases on composite indicator scores. The research results show that the SAW-Max-Entropy weighting scheme achieves greater discriminating power than weighting schemes based on the Entropy Index, Expert Opinion, and Equal Weights. The SAW-Max-Entropy method has high application potential due to the increasing use of composite indicators across diverse areas of knowledge. Additionally, the method represents a robust response to the challenge of constructing composite indicators with superior discriminating power.

This paper seeks to understand how decision-making works at the first appearance hearings (Custody Hearings) in Brazil, an initiative that intends to make the Brazilian criminal justice system more accusatorial. We used primary data gathered in the hearings between April and December 2018 in nine Brazilian states. Binary logistic regression models were applied to identify the variables that affect the odds ratios of pretrial detention. Results indicated a high level of homology between the prosecutors' requests and the judges' decisions, even when controlling for the characteristics of offense and offender, which precludes any direct openness to the defense. Decision-making in the Custody Hearing reinforces the inquisitorial characteristics and the institutional features of the Brazilian Criminal Justice System, suggesting that the reforms carried out over the last years were not able to change how actors operate on a daily basis.

This research proposes a decision framework that allows for the identification of the most suitable methods to construct stable composite indicators that capture the concept of multidimensional social phenomena. This decision framework is applied to discover which method among six best represents the social exclusion of eight medium-sized Brazilian cities. The results indicate that space is important in the definition and performance of the method, and ease methods to apply present the best performance. However, one of them fails to capture the concept of the multidimensional phenomenon in two cities. The research makes six important contributions to the literature. First, it offers a decision framework for choosing the best-fit method to construct a composite social indicator. Second, it shows to what extent geographic space matters in defining the best-fit method. Third, it identifies the best-fit method regarding stability and linkage with the conceptually most significant indicator of social exclusion. Fourth, it reveals the methods to be avoided, given their poor performance. Fifth, it indicates the mathematical properties that best represent composite social phenomena. Sixth, it illuminates the debate on social exclusion from a geographical and public policy perspective.

This paper seeks to identify the chief factors behind the incidence of COVID-19 cases and deaths inside the Brazilian prison system, testing the hypotheses that better equipped and better-staffed systems and the adoption of the prophylactic measures lead to lower incidence and death rates. Results show partial support for these expectations, as certain infrastructure-related measures such as the number of medical appointments held at the prison facility, the percentage of establishments not directly managed by the State, and the number of laboratory tests by inmate are statistically associated with the number of recorded COVID-19 cases. In turn, the number of provisional detainees, and the percentage of prison units without educational facilities are significant predictors of COVID-19 deaths. Nonetheless, not a single measure adopted to prevent COVID-19 inside prisons and jails stood the empirical testing. These results have important policy and research implications.

Este artigo apresenta alguns resultados da comparação da estrutura socioespacial das 15 principais regiões metropolitanas brasileiras e de sua evolução no período entre 2000 e 2010. A análise aborda prioritariamente o fenômeno de concentração dos grupos de mais alto status nos espaços superiores dessas metrópoles, configurando a sua hipersegregação. Hipóteses explicativas para esse processo são apresentadas a partir da análise específica da Região Metropolitana de Belo Horizonte, cuja composição social é muito próxima da composição média das 15 RMs estudadas e para a qual há dados específicos sobre dinâmica imobiliária, índice de qualidade de vida e mobilidade residencial. A histórica atratividade das áreas centrais intensificou-se na década de 2000, mantendo-as como localização preferencial dos investimentos imobiliários de alto padrão.