Brazilian scientific productivity from a gender perspective during the Covid-19 pandemic: classification and analysis via machine learning



COVID-19, scientific production, gender classification, machine learning


Scientific research activities, in general, have been affected due to the COVID-19 pandemic and the need for distancing. In this paper, an analysis of the impact of COVID-19 on Brazilian scientific research is made, examining the number of complete manuscripts published in the period from 2018 to 2021, considering the researcher's gender. A crawler is implemented to extract the names of Brazilian researchers from the articles, and some machine learning models (SVM, BiLSTM, and CNN) are applied to classify the authors' gender. Some models are able to accurately predict gender in more than 95% of cases. In addition, we verified that in 2021 there was a drop of 37.47% in the publications of articles by Brazilian researchers. The results indicate that there was a greater drop in publications for females in most machine learning models applied, corroborating differences in the distribution of household activities and family care between the two genders.


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Author Biographies

Rosana Cibely B. Rego, Universidade Federal Rural do Semi-Arido (UFERSA), Mossoró - RN, 59625-900

Professor at the Department of Engineering and Technology at the Universidade Federal Rural do Semi-Árido and PhD in Electrical and Computer Engineering at the Federal University of Rio Grande do Norte (2022), with research in the area of Intelligent Control, Neural Control, Neural Networks, Learning of machine. Certified by Huawei ICT Academy in artificial intelligence. She has knowledge in the programming area (C/C++, Java, Python, Fortran, MatLab/Scilab).

Gabriel da Silva Nascimento, Federal Institute of Education, Science and Technology of Paraíba, João Pessoa - PB, 58015-020

Graduating in Computer Engineering at the Federal Institute of Education, Science and Technology of Paraíba. Technical education in Telecommunications from the Escola Técnica Redentorista in Paraíba. Knowledge in the area of programming (Python, C/C++). Works in the area of optimization software development with python 3. Interest in research in the areas of artificial intelligence, embedded systems and software development.

Davi Emmanuel de Lima Rodrigues, Universidade Federal Rural do Semi-Arido (UFERSA), Mossoró - RN, 59625-900

Graduating in Science and Technology at the Federal Rural University of the Semi-Arid. Working on the research project: PEH30001-2021 - Scientific Productivity from a gender perspective during the Covid-19 pandemic (UFERSA). Knowledge of programming (Python, C, Java, and Javascript). Interest in research in the areas of artificial intelligence, WEB, and mobile development.

Samara Martins Nascimento, Universidade Federal Rural do Semi-Arido (UFERSA), Mossoró - RN, 59625-900

PhD in Computer Science, Federal University of Ceará. Adjunct Professor at the Federal Rural University of the Semi-Arid (UFERSA). She is one of the leaders of the Research Groups Laboratory of Software Innovations (LIS) and Laboratory of Computational Intelligence (CiLab). Her main areas of interest are Database, Big Data, Data Streams, NoSQL Databases, Data Warehouse, Data Management, Systems Analysis, Software Quality and Software Metrics.

Verônica Maria L. Silva, Universidade Federal da Paraiba, PB, 58051-900

Graduated in Computer Engineering from the Federal University of Ceará (2011). Since 2015, she has been a professor at the Federal University of the Semi-Árido Rural (UFERSA) and a PhD in Electrical Engineering from the Federal University of Campina Grande (UFCG), 2019. Her research interests include digital systems, analog-digital converters, analog-to-information converters and embedded systems, artificial intelligence.


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How to Cite

Rego, R. C. B., Nascimento, G. da S., Rodrigues, D. E. . de L., Nascimento, S. M., & Silva, V. M. L. (2022). Brazilian scientific productivity from a gender perspective during the Covid-19 pandemic: classification and analysis via machine learning. IEEE Latin America Transactions, 21(2), 302–309. Retrieved from