The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis

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dc.creatorAndré Luís Ribeiro
dc.creatorOthávio Ruddá Araújo
dc.creatorLeonardo B. Oliveira
dc.creatorMagna Maria Inácio
dc.date.accessioned2023-10-26T20:20:19Z
dc.date.accessioned2025-09-09T01:12:39Z
dc.date.available2023-10-26T20:20:19Z
dc.date.issued2022-07-21
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0271741
dc.identifier.issn1932-6203
dc.identifier.urihttps://hdl.handle.net/1843/60108
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofPLoS ONE
dc.rightsAcesso Aberto
dc.subjectPolíticas públicas
dc.subjectPresidentes - Sucessão
dc.subjectAprendizado do computador
dc.subjectBrasil - Política e Governo
dc.subject.otherAdministrative Decrees
dc.subject.otherPresidents
dc.subject.otherMachine Learning
dc.subject.otherBrazil politics
dc.titleThe Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis
dc.typeArtigo de periódico
local.citation.epage31
local.citation.issue7
local.citation.spagee0271741
local.citation.volume17
local.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.
local.identifier.orcidhttps://orcid.org/0000-0001-7691-2100
local.identifier.orcidhttps://orcid.org/0000-0003-1747-8820
local.publisher.countryBrasil
local.publisher.departmentFAF - DEPARTAMENTO DE CIÊNCIA POLÍTICA
local.publisher.departmentFAFICH - FACULDADE DE FILOSOFIA E CIENCIAS HUMANAS
local.publisher.departmentICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
local.publisher.initialsUFMG
local.url.externahttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0271741

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