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

This 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 decre...

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Autores principales: Ribeiro, André Luís, Araújo, Othávio Ruddá, Oliveira, Leonardo B., Inácio, Magna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302789/
https://www.ncbi.nlm.nih.gov/pubmed/35862360
http://dx.doi.org/10.1371/journal.pone.0271741
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author Ribeiro, André Luís
Araújo, Othávio Ruddá
Oliveira, Leonardo B.
Inácio, Magna
author_facet Ribeiro, André Luís
Araújo, Othávio Ruddá
Oliveira, Leonardo B.
Inácio, Magna
author_sort Ribeiro, André Luís
collection PubMed
description This 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.
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spelling pubmed-93027892022-07-22 The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis Ribeiro, André Luís Araújo, Othávio Ruddá Oliveira, Leonardo B. Inácio, Magna PLoS One Research Article This 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. Public Library of Science 2022-07-21 /pmc/articles/PMC9302789/ /pubmed/35862360 http://dx.doi.org/10.1371/journal.pone.0271741 Text en © 2022 Ribeiro et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ribeiro, André Luís
Araújo, Othávio Ruddá
Oliveira, Leonardo B.
Inácio, Magna
The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis
title The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis
title_full The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis
title_fullStr The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis
title_full_unstemmed The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis
title_short The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis
title_sort executive branch decisions in brazil: a study of administrative decrees through machine learning and network analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302789/
https://www.ncbi.nlm.nih.gov/pubmed/35862360
http://dx.doi.org/10.1371/journal.pone.0271741
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