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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/60108
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dc.creatorAndré Luís Ribeiropt_BR
dc.creatorOthávio Ruddá Araújopt_BR
dc.creatorLeonardo B. Oliveirapt_BR
dc.creatorMagna Maria Ináciopt_BR
dc.date.accessioned2023-10-26T20:20:19Z-
dc.date.available2023-10-26T20:20:19Z-
dc.date.issued2022-07-21-
dc.citation.volume17pt_BR
dc.citation.issue7pt_BR
dc.citation.spagee0271741pt_BR
dc.citation.epage31pt_BR
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0271741pt_BR
dc.identifier.issn1932-6203pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/60108-
dc.description.resumoThis paper dissects the potential of state-of-the-art computational analysis to promote the investigation of government’s administrative decisions and politics. The Executive Branch generates massive amounts of textual data comprising daily decisions in several levels and stages of the law and decree-making processes. The use of automated text analysis to explore this data based on the substantive interests of scholars runs into computational challenges. Computational methods have been applied to texts from the Legislative and Judicial Branches; however, there barely are suitable taxonomies to automate the classification and analysis of the Executive’s administrative decrees. To solve this problem, we put forward a computational framework to analyze the Brazilian administrative decrees from 2000 to 2019. Our strategy to uncover the contents and patterns of the presidential decree-making is developed in three main steps. First, we conduct an unsupervised text analysis through the LDA algorithm for topic modeling. Second, building upon the LDA results, we propose two taxonomies for the classification of decrees: (a) the ministerial coauthorship of the decrees to map policy areas and (b) the decrees’ fields of law based on a tagging system provided by the Brazilian Senate. Using these taxonomies, we compare the performance of three supervised text classification algorithms: SVM, Convolutional Neural Network, and Hierarchical Attention Network, achieving F1-scores of up to 80% when automatically classifying decrees. Third, we analyze the network generated by links between decrees through centrality and clustering approaches, distinguishing a set of administrative decisions related to the president’s priorities in the economic policy area. Our findings confirm the potential of our computational framework to explore N-large datasets, advance exploratory studies, and generate testable propositions in different research areas. They advance the monitoring of Brazil’s administrative decree-making process that is shaped by the president’s priorities and by the interplay among cabinet members.pt_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentFAF - DEPARTAMENTO DE CIÊNCIA POLÍTICApt_BR
dc.publisher.departmentFAFICH - FACULDADE DE FILOSOFIA E CIENCIAS HUMANASpt_BR
dc.publisher.departmentICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃOpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofPLoS ONEpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectAdministrative Decreespt_BR
dc.subjectPresidentspt_BR
dc.subjectMachine Learningpt_BR
dc.subjectBrazil politicspt_BR
dc.subject.otherPolíticas públicaspt_BR
dc.subject.otherPresidentes - Sucessãopt_BR
dc.subject.otherAprendizado do computadorpt_BR
dc.subject.otherBrasil - Política e Governopt_BR
dc.titleThe Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysispt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271741pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-7691-2100pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-1747-8820pt_BR
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