Um modelo para avaliação de relevância científica baseado em métricas de análise de redes sociais

• Ano de defesa: 2015
• Autor(a) principal: Wanderley, Ayslânya Jeronimo
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: Ciência dos dados; Data Science; Avaliação de Pesquisadores; Regressão logística; Análise de Redes Sociais; Researchers assessment; Logistic Regression; Social Network Analysis; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/7850

The task of assessing the scientific relevance of a researcher is not always trivial. Generally, this process is based on indices that consider the production and the impact of it in their area of research. However, the literature indicates that such indicators taken separately are insufficient, since they ignore the standards of relationship in which researchers are inserted. In addition, many studies have proven that collaborative relationships have a serious impact on the relevance of a researcher. In this context, it is understood that the modeling and analysis of these relationships can help building new indicators that complement the current evaluation process. Thus, this work aimed to specify a statistical model which allows for assessing the scientific relevance of a researcher, defined by the detention of productivity grant from the National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq), based on metrics applied to their scientific collaboration networks. Therefore, we applied metrics of Social Network Analysis (SNA) to collaborative networks of 1592 professors connected with Postgraduate Program in Computer Science area that later served as the basis for construction of a logistic regression model using the stratified 10-fold cross-validation technique. The proposed model produced very encouraging results and demonstrated that the SNA metrics that most influence in assessing the relevance of a researcher are the Betweenness Centrality,Weighted Degree, PageRank and Local Clustering Coefficient, having the first two positive influence and the last two negative influence. This shows that researchers who play an intermediary role within the network and usually maintain strong relationships with its collaborators are more likely to be contemplated with productivity grants, while those researchers with a more cohesive network and often collaborate with researchers who are already leaders in their field are less likely to be a scholarship student.