Intellectual and social similarity among scholarly journals: An exploratory comparison of the networks of editors, authors and co-citations

Abstract

This paper explores, by using suitable quantitative techniques, to what extent the intellectual proximity among scholarly journals is also a proximity in terms of social communities gathered around the journals. Three fields are considered: statistics, economics and information and library sciences. Co-citation networks (CC) represent the intellectual proximity among journals.The academic communities around the journals are represented by considering the networks of journals generated by authors writing in more than one journal (interlocking authorship: IA), and the networks generated by scholars sitting in the editorial board of more than one journal (interlocking editorship: IE). For comparing the whole structure of the networks, the dissimilarity matrices are considered. The CC, IE and IA networks appear to be correlated for the three fields. The strongest correlations is between CC and IA for the three fields. Lower and similar correlations are obtained for CC and IE, and for IE and IA. The CC, IE and IA networks are then partitioned in communities. Information and library sciences is the field where communities are more easily detectable, while the most difficult field is economics. The degrees of association among the detected communities show that they are not independent. For all the fields, the strongest association is between CC and IA networks; the minimum level of association is between IE and CC. Overall, these results indicate that the intellectual proximity is also a proximity among authors and among editors of the journals. Thus, the three maps of editorial power, intellectual proximity and authors communities tell similar stories.

“Many‐citedness”: Citations Measure More Than Just Scientific Quality

“…But, in general, there are very few large scale empirical studies of citations networks in economics at the author level. While most author‐level network analyses focus on coauthorship and productivity, citation networks have been studied at the paper level (e.g., by Seabrooke et al ., 2015), institution level (Önder and Terviö, 2015), or journal level (e.g., Cronin, 2008; Baccini et al ., 2020). Author‐level studies, as attempted in what follows, may provide unique information for their ability to consider correlates of citations at the individual scientist level, but so far in economics, they have involved small samples only or selected communities of economists, such as economic historians (Galofré‐Vilà, 2020) or the Chicago School (Henriksen et al ., 2017).…”

Peer-making: The interconnections between PhD thesis committee membership and copublishing

“…On the one hand, the very heuristic nature of networks can bring to light some other elements structuring the scientific world, which other approaches cannot reveal. From this particular methodological perspective, our work is the continuation of other contemporary research using network analysis in Science Studies (Milard, 2014, Baccini et al, 2020. By combining "bibliometric data" and co-membership and co-publishing network analysis, our method reveals both the relationships which are at the background of the composition of thesis committees but also reveals the "mentors'" relationships to the PhD students they supervise or whose final research work they assess.…”

Geography of scientific knowledge: A proximity approach

“…These initial findings call for more refined research on citation patterns based on different notions of proximity, opening up a new, theoretically informed research program in the (quantitative) studies of science, which can also leverage past research on proximity in innovation studies (Balland et al, 2015). Here, the geography of scientific knowledge production can build on a longer tradition in science studies on the measurement of cognitive proximity using co-occurrence data (Van Eck & Waltman, 2009) and can find inspiration in more recent attempts to combine physical, cognitive, and social proximity measures in a single research design (Baccini, Barabesi, et al, 2020;Head et al, 2019;Wuestman et al, 2019).…”