As social fields, scientific networks are often characterised by systemic inequalities, with authors accumulating (dis)advantages based on ascribed and/or acquired sociodemographic characteristics (Merton 1968; 1988). Disadvantages, such as those involving the interactions and relationships of researchers (de Solla Price 1965), are often represented in empirical settings through formal channels, such as references in scientific works, co-authorships and mentorship relationships among others (Gläser 2001). These systematic disadvantages can be explained through the analysis of local network mechanisms at the micro-level (Crane 1972; Mullins and Mullins 1973; Chubin 1976; Wuchty et al. 2007). Such mechanisms can shape inequalities at the macro-level, leading to tendencies such as preferential attachments (de Solla Price 1965; Barabási and Albert 1999; Newman 2001). Some of these disadvantages can been seen in the prevalence or absence of micro-mechanisms. In this research, we explore how gender and the country of institutional affiliation of published authors are less prevalent in two prominent journals of social network science.
This research considers how some attributes, such as gender and country of institutional affiliation, shape the network of core-set (Collins 1974) journals in social network science. The framework used relies on the relevance of these network micro-mechanisms—as explicit internal structures of entities and relations represented as subgraphs (Stadtfeld and Amati 2021)—also called local configurations (Lusher et al. 2012). In this research, processes are investigated in the frame of the sociology of science and knowledge (Crane 1972; Schrum and Mullins 1988) by explicitly stating the relevance of attributes in creating cumulative (dis)advantages (Cole and Cole 1973; Merton 1988), local structures such as dyads and triads (Mullins 19721973; Mullins and Mullins 1973) or similar structural positions in the network (White and Breiger 1975; Breiger 1976; Mullins et al. 1977). These micro-mechanisms were examined to understand the evolution of scientific networks, often called ‘invisible colleges’ (de Solla Price 1965; Crane 1972) or specialities (White and Breiger 1975; Breiger 1976; Mullins et al. 1977; Burt and Doreian 1982). Recent investigations aim to revitalise this line of research to understand how social ties form and why networks arise from simultaneously operating micro-mechanisms (e.g., Kronegger et al. 2012; Dahlander and McFarland 2013; Ferligoj et al. 2015; Zinilli 2016; Sciabolazza et al. 2017; Gondal 2018; Akbaritabar et al. 2020; Purwitasari et al. 2020).
Previous research has done extensive work to characterise and understand the evolution of the social network science perspective (Mullins and Mullins 1973; Freeman 2004; Scott 2011; Batagelj et al. 2014; Maltseva and Batagelj 2019). However, as far as we are aware, the framework of considering simultaneously operating micro-mechanisms has not yet been applied to the field of social network science.
In this research, we address the prevalence of two micro-mechanisms in relation to two core-set journals of the social network science community, Social Networks and Revista Hispana para el Análisis de Redes Sociales (REDES). Specifically, we focus on understanding how gender and authors’ countries of institutional affiliation shape the networks of these journals. The strategy used for data collection involved manually identifying the gender and country of institutional affiliation of each author. The analysis also applied an ERGM model. In total, we reviewed a two-mode network of 387 papers with 874 authors. Our main results indicate that the country of institutional affiliation creates a centre-periphery tendency in both journals, and there was a tendency for women to be under-represented in published papers during the period under analysis.
The following article presents its argument in four sections. First, we begin with the literature review to examine previous research on gender and institutional affiliation of authors and its relationship with publication. Second, we describe the material and methods applied, including data collection and analysis strategies. Third, we show the results obtained and discuss the main results. Finally, we draw some conclusions, address their limitations and present advice for further research.
The social network perspective (Mitchell et al. 1969; Wasserman and Faust 1994; Freeman 2004), also known as (social) network science (Brandes et al. 2013; Newman 2018), is a field that has been fundamental to the understanding of scientific networks (e.g., de Solla Price 19631965; Crane 1972; Mullins 19721973; Chubin 1976; Schrum and Mullins 1988; Barabási and Albert 1999; Newman 2001). Social network science has been considered an area where members share epistemic perspectives and practices (Knox et al. 2006; Venturini et al. 2019), being described previousley as an (in) visible college (Freeman and Freeman 1980; Hummon and Carley 1993; Freeman 2004; Maltseva and Batagelj 2019). In recent years, this field has expanded and evolved significantly in the intersection between the social sciences and other disciplines, such as physics (Bonacich 2004; Lazer et al. 2009; Brandes and Pinch 2011; Freeman 2011; Scott 2011; Batagelj et al. 2014), ‘data science’ (Shafie and Brandes 2018) or the study of social animals (Maltseva and Batagelj 20192021). The field has created new bridges between professionals in different disciplines, as demonstrated in the Networks 2021 conference involving both social scientists (who often gather in the SUNBELT conference and the International Network for Social Network Analysis) and the so-called natural sciences (usually congregating at NetSci conferences and the Network Science Society).
While extensive effort has been made to understand the development of the social network perspective, there is scarce research into the sociodemographic attributes of researchers and how these can lead to disadvantages. In particular, there is comparatively less understanding of women researchers and researchers affiliated to institutions in the Global South.
Recent research had started to fill this gap and reconstruct the history of the social network perspective in communities such as Iberian America (Espinoza 2005; Molina 2007; Ortiz et al. 2021; Vélez et al. 2021) and countries from Latin America, such as Argentina (Tevez and Pasarin 2014), Brazil (Varanda et al. 2012), Colombia (Palacio and Vélez 2014), Chile (Gaete and Pino 2014) and Mexico (Ramos et al. 2014). This community has been characterised as demanding, active, critical and one that is still in process of learning (Gaete and Pinto 2014). The interdisciplinary nature and diversity of topics for research has also been highlighted (Tevez and Pasarin 2014). Some countries, like Argentina and Mexico, have a longer tradition with the network’s community (Molina 2007; Tevez and Pasarin 2014; Ramos et al. 2014), which makes them more connected to the global community. Moreover, a particular aspect of this community is that its literature has started to be translated over the past two decades (Molina 2007). A special case is Brazil, which is historically linked to Portugal and has a shared language (Varanda et al. 2012). The Hispanic community had also become more visible from its contributions to the journal REDES and the SUNBELT session “Mesa hispana sobre análisis de redes sociales” (Ramos et al. 2014; Ortiz et al. 2021).
Current initiatives, such as Women in Network Science, have started to address the underrepresentation and recognition of women, transgender and non-binary network scientists in this field. However, there is no clear understanding of the local mechanisms responsible for the emergence of the network of social network researchers, particularly considering sociodemographic characteristics, such as gender and the country to which researchers are institutionally affiliated.
Articles are the primary way of communicating within scientific communities, which aim to advance science, report new discoveries and build on previous research. However, the availability of documents and the underlying social patterns that shape science publication can blur or distort a scientific fields’ trends, giving more (dis)advantages to some social groups over others (Cole and Cole 1973; Merton 1988). One of these social characteristics is the ascribed gender of researchers, with certain groups been underrepresented in scientific contribution. For example, in academic awards, there has been a persistent gender gap in life sciences, computer science and mathematics, where women have been less favoured (Meho 2021). Chatterjee and Werner (2021) found gender disparities in citations between men and women in high-impact medical journals, where papers written by women received fewer citations than men. Moreover, women who did win awards tended to receive less money and prestige for their discoveries, as shown in the case of biomedical awards (Ma et al. 2019).
Some studies have concentrated on gender disparities in science using papers as a proxy for scientific collaborations. However, previous studies have already identified some gendered stratification patterns in areas of recruitment, socialisation of young investigators, access to publications, recognition (of citations and awards) and research facilities (Zuckerman 1970). Historically, compared to men, women tend to publish fewer articles as first or last authors (Huang et al. 2020). There can also be a more profound disparity depending on the discipline they work in. For example, in engineering, it has been found that male engineers produce 80% of its scientific publications, and while female engineers tend to publish in journals with higher impact factors, they appear to get less recognition (Ghiasi et al. 2015). Studies have established that men dominate scientific publication in almost every country of the world (Larvière et al. 2013). Globally, women have less than 30% of fractionalised authorships, and for each article in which a woman is the first author, there are almost two led by men (Sugimoto 2013). The authors’ place in the authorship could signify the importance of their contributions to the paper, however it has been found that women contribute in more practical tasks, like in the development of experiments (Macaluso et al. 2016). While it has been shown that gender disparities exist in science and the scientific community, as far as we know, there are few studies that consider these disparities in the context of (social) network science.
One of the main indicators of gender inequality in science is the number of publications each group completes. Previous research had demonstrated that women tend to publish less (Larvière et al. 2013; Athanasiou et al. 2016; Cainelli et al. 2015). Therefore, we envisage that:
Two-mode networks of published authors shall demonstrate that women publish less papers than men.
Institutional affiliation (country)
If we focus on the progress of scientific collaboration, we should also consider the country of an author’s institutional affiliation. This indicator can show us if the country makes a difference when publishing research in the community. Gazni et al. (2011) studied 14,000,000 documents from the Web of Science to look deeply into international collaborations. Their results suggest that Western countries are situated at the core of their map and that they collaborate extensively with one another. Also, they found that high-impact institutions collaborate more often than low-impact institutions, as previously stated (e.g., Wagner and Leydesdorff 2005). Another study (Murray et al. 2019) indicates that this type of behaviour also happens among reviewers: they found higher rates of acceptance of an article when gender was the same and when the gatekeeper and the corresponding author also came from the same country. Another research study by Sugimoto et al. (2015) contrasts the development indicators of a country with gender to evaluate possible disparities. Their conclusions showed that countries with low levels of development had the lowest participation of women in science and less engagement internationally. These antecedents appear to be crucial to an author’s propensity for collaboration in science.
The country of the author’s institutional affiliation can have different structural advantages or disadvantages associated with it, which may help provide information about this disparity. Hence, we envisage that:
Two-mode networks of published authors shall demonstrate that researchers affiliated with institutions from the Global South publish less papers than researchers affiliated with institutions from the Global North.
To categorise each country, we grouped them into countries from the Global South or Global North. We used the definition proposed by Bonaventura de Sousa Santos and Maria Paula Meneses (2014) for this process. Accordingly, the Global South countries (1) had been colonised by other countries during their history and (2) are geographically situated in America, Africa or Asia. Countries like Sweden, the United States, the United Kingdom, Italy and Luxemburg were categorised as part of the core, or Global North. Meanwhile, Chile, México and Argentina, among others, were considered periphery or members of the Global South.
While our hypotheses mainly focus on the attributes that create comparative (dis)advantages (Cole and Cole 1973; Merton 1988), we will also control for the presence of network mechanisms. Social network mechanisms can be classified into different types of structures, such as relational, assortativity or proximity-based mechanisms (Rivera et al. 2010), which are often operationalised into less complex and concrete micro-mechanisms expressed in subgraphs (Stadtfeld and Amati 2021). Relational mechanisms are often based on structures that consider direct or indirect paths of actors within a network, such as dyads and triads (Wasserman and Faust 1994). While assortativity mechanisms, on the other hand, rely on the (dis)similarity of actors in creating social ties, such as homophily (Lazarsfeld and Merton 1954; McPherson 2001). Finally, the proximity mechanisms are based on the importance of the physical and cultural environment to shape networks, such as the tendency of actors to create ties when they share different types of activities (Feld 19811982).
One of the most recurrent and frequently used mechanisms in the context of scientific networks is the Matthew effect or peer recognition (Zuckerman 1967; Merton 1968), also known as preferential attachment (de Solla Price 1965; Barabási and Albert 1999; Newman 2001). At the level of micro-mechanisms and in the context of a network perspective, the Matthew effect is often operationalised as (in) degree effects (Borgatti and Balgin 2011) following a relational-based type of mechanism. However, when a two-mode network is considered, as the relationships between authors publishing papers, the effects can be dissected into two complementary processes in the frame of a proximity-based mechanism. The former is often referred as weighted degree distribution of researchers or alternating-author-k-star, which captures the effect of multiple papers authored by the same researcher when authors and their works are considered. Previous research using a two-mode network of authors citing papers interpreted this effect as a type of preferential attachment to citing reputable authors that are visibly cited by other scholars (Gondal 2011). The second effect is the weighted degree distribution of publications or alternating-paper-k-star, which for the context of authorship networks often refers to the variation in the level of productivity of the authors. For other contexts that measure citation, this effect is considered as the tendency to cite the same multiple other authors or bibliographic coupling (from the perspective of the papers) (Gondal 2011). Path distances, on the other hand, are often used to identify levels of ‘overlapping chains’ instead of dense clusters. However, in relatively small collaboration networks, it might be difficult to have sufficient variation of authors publishing shared papers. Hence, a simple two-path effect measure can be used instead to control for the extent to which two papers are published from the same author.
These effects can be considered as a proximity-based mechanism, as the frame of a scientific paper often relies on a common activity shared by the authors. Feld (1981) considered the focus of activities as contexts in which activities are organised, consisting in several join focuses of activities (e.g., workplaces, voluntary organisations, hangouts, families, among others) and individuals that actively bring people together or passively constrain them to interact. These focuses of activities are often represented as a two-mode network (Borgatti and Halgin 2011). Previous research has considered projects for team formation as a focus of activities (Zhu et al. 2013), and it could be argued that papers are often the results of previously shared common activities.
Finally, assortativity-based mechanisms are often explored considering homophily between the actors. Homophily, a tendency of people to create ties with those that share similar attributes (Lazarsfeld and Merton 1954), has been extensively studied in empirical research (McPherson 2001). For the context of scientific collaboration, previous research has emphasised the importance of similarities, such as in gender, language, ethnicity, joint affiliation within colleges or departments, spatial proximity and similarity between topics or disciplines (Kronegger et al. 2012; Dahlander and McFarland 2013; Cimenler et al. 2015; Peng 2015; Dhand et al. 2016; Luke et al. 2016; Zinilli 2016; Fagan et al. 2018; McLevey et al. 2018; Wang et al. 2018; Akbaritabar et al. 2021). However, in empirical analysis, these attributes can only be tested if publication data can be retrieved, while extra assumptions are required to cluster research areas into similar topics or disciplines.
The aforementioned micro-mechanisms are often represented using simultaneously operating subgraphs that together resemble the examined network (Robins et al. 2005). More concretely, the assumption of micro–macro linkage is often explored by simulating networks that are constrained by the estimated parameters corresponding to specific statistics (i.e., micro-mechanisms) in a random network that aims to replicate the observed system (i.e., the ‘macro level’). A formal way of evaluating the linkage between these analytical levels is by using statistical goodness-of-fit for social networks (Snijders and Steglich 2015; Stadtfeld 2018). These diagnostics compare observed features of the networks that are not directly included in the model, with a simulated population of networks that are constrained by the parameters (Hunter et al. 2008). If the model is not capable of reproducing some of the structures of the complete network using these micro-mechanisms, then is often believed that the model is not accurately representing the observed network.