A bibliometric analysis of the global value chains research field

This paper presents a bibliometric analysis of the global value chains (GVC) research field. To identify the most influential authors and contributions, potential collaboration networks, most discussed topics, and areas of further research opportunities within or related to the GVCresearch field, we applied the five most common bibliometric methods, namely citation, co-citation, co-author, and co-word analysis, and bibliometric coupling method. Our dataset for quantitative analysis of available articles, authors, and publication outlets in the GVC research field includes 2,506 articles, book chapters, books, and conference papers from 1,047 different sources in the Web of Science database published between the years 1999 and 2021. Our analysis provided a structured and thorough bibliometric overview of the GVC research field, including the years of the COVID -19 pandemic. The results show that the most frequently researched topics include GVC governance, trade, innovation, and production networks. We also identified future GVC-related bibliometric research streams, such as linking GVCs to international sourcing, corporate functions, and firm performance

First-mover advantage explains gender disparities in physics citations

Mounting evidence suggests that publications and citations of scholars in the STEM fields (Science, Technology, Engineering and Mathematics) suffer from gender biases. In this paper, we study the physics community, a core STEM field in which women are still largely underrepresented and where these gender disparities persist. To reveal such inequalities, we compare the citations received by papers led by men and women that cover the same topics in a comparable way. To do that, we devise a robust statistical measure of similarity between publications that enables us to detect pairs of similar papers. Our findings indicate that although papers written by women tend to have lower visibility in the citation network, pairs of similar papers written by men and women receive comparable attention when corrected for the time of publication. These analyses suggest that gender disparity is closely related to the first-mover and cumulative advantage that men have in physics, and is not an intentional act of discrimination towards women.Comment: 21 pages, 8 tables, 10 figure

A Modeling and Analysis Framework for Knowledge System Based on Meta-Network Approach

Nowadays some online research platforms (e.g., Web of Science or IEEE Xplore) provide bibliographic content and tools to access, analyze, and manage the world\u27s leading journals and conference proceedings in sciences, social science, arts, and humanities. However, when facing increasingly mass literature, it’s very difficult for researchers to effectively and systematically acquire the knowledge structure about a particular topic by using traditional literature reviewing method. Therefore we need explore new knowledge discovery tools for knowledge representation in an effective and efficient way. This paper proposes a knowledge system meta-network model by identifying the concepts representing entities and relationships from bibliometric data, and a methodology framework for meta-network modeling and analysis by using integrated techniques, including text mining, network text analysis, social network analysis, longitudinal network analysis and visualization. Case study using the Web of Science database as data source, explores the knowledge structure and interdisciplinary cooperation mode, as well as hot topics evolution in the field of World Trade Web

Algorithms For Discovering Communities In Complex Networks

It has been observed that real-world random networks like the WWW, Internet, social networks, citation networks, etc., organize themselves into closely-knit groups that are locally dense and globally sparse. These closely-knit groups are termed communities. Nodes within a community are similar in some aspect. For example in a WWW network, communities might consist of web pages that share similar contents. Mining these communities facilitates better understanding of their evolution and topology, and is of great theoretical and commercial significance. Community related research has focused on two main problems: community discovery and community identification. Community discovery is the problem of extracting all the communities in a given network, whereas community identification is the problem of identifying the community, to which, a given set of nodes belong. We make a comparative study of various existing community-discovery algorithms. We then propose a new algorithm based on bibliographic metrics, which addresses the drawbacks in existing approaches. Bibliographic metrics are used to study similarities between publications in a citation network. Our algorithm classifies nodes in the network based on the similarity of their neighborhoods. One of the drawbacks of the current community-discovery algorithms is their computational complexity. These algorithms do not scale up to the enormous size of the real-world networks. We propose a hash-table-based technique that helps us compute the bibliometric similarity between nodes in O(m ?) time. Here m is the number of edges in the graph and ?, the largest degree. Next, we investigate different centrality metrics. Centrality metrics are used to portray the importance of a node in the network. We propose an algorithm that utilizes centrality metrics of the nodes to compute the importance of the edges in the network. Removal of the edges in ascending order of their importance breaks the network into components, each of which represent a community. We compare the performance of the algorithm on synthetic networks with a known community structure using several centrality metrics. Performance was measured as the percentage of nodes that were correctly classified. As an illustration, we model the ucf.edu domain as a web graph and analyze the changes in its properties like densification power law, edge density, degree distribution, diameter, etc., over a five-year period. Our results show super-linear growth in the number of edges with time. We observe (and explain) that despite the increase in average degree of the nodes, the edge density decreases with time

Produção Científica sobre Capital Social:estudo por acoplamento bibliográfico

Currently, the definition ofSocial Capitalhave differentapproaches and, as a consequence, several applicationsin scientificresearch of different fields. Based on this,we intend toexplore theSocialCapital’s content in this scientific production, cognizing which authorswere chosenin used references as well as which keywords, institutions, andcountries workwith the due term within thescientific field of Information Science. Assuming thatthe citations ofscientific papersrepresent an importantsource ofinformationfor researchers, andserveas a tool forperformance assessment of thescientific outputof a certain field, we analyzed the bibliographiccitationsof theliterature corpus dealing with “SocialCapital”through bibliographiccoupling.The analyzed bibliographic productionwaspublishedbetween 2005and 2013andlisted in theWebof Science(WoS). The softwaresBiblioTools®andGephi® were used for the analysisof bibliographiccoupling and the subsequent corpus analysis. In conclusion, we realized that bibliographiccoupling andco-occurrence(author, keywords, institutions, etc.) are importantmethodsin mapping scientific production and creating maps which canbecomevaluable tools for perception,assessment and managementof scientificproduction on certain topicsfor differentscientific fields.Atualmente, a definição de Capital Social possui diferentes enfoques e, como consequência, aplicações diversas nas pesquisas científicas em diferentes áreas.Com base nisso, pretendemos situar a produção científica que explora a noção de Capital Socialem seu conteúdo, percebendo quais autores foram escolhidos nas referências utilizadas, assim como quais palavras-chave, instituições e países trabalham com o termo dentro do campo científico da Ciência da Informação.Partindo da premissa que as citações dos artigos científicos constituem uma importante fonte de informaçõespara pesquisadores e servem comoinstrumento de avaliação do comportamento da produção científica de um campo, analisamos as citações bibliográficas da literaturaque trata do termo Capital Social por meio do acoplamento bibliográfico.A produção bibliográfica analisadafoi publicada entre 2005 e 2013 e repertoriada na Web of Science (WoS). Para a análise do acoplamento bibliográfico e posterior análise do corpus, foram utilizados os softwares livres BiblioTools® e Gephi®.Concluindo, percebemos que o acoplamento bibliográfico e a coocorrência (de autores, palavras-chave, instituições, etc.) são métodos importantes no mapeamento da produção científica, produzindo mapas que podem se tornar ferramentas valiosas na percepção, avaliação e gestão da produção científica de determinados temas para diferentes campos científicos