As this paper includes the word “women” in its title, we must reflect upon what we mean by this category. First of all, it is crucial to state that most of the women we are referring to were white and came from upper- and middle-class backgrounds. This, in fact, conditioned women’s survival in the intellectual and cultural fields. Although, on the one hand, their condition as women inevitably limited their social roles, their position was, on the other hand, very privileged compared to that of other women who were not white or from the upper-middle class. Silvia Federici notes that as long as the sexual division of labour in the framework of analysis remained as powerful as it had been since the emergence of capitalism, the concept “women” remains a useful category for analysis. Federici’s work has led us to realise the importance of women’s strategies to legitimise their voices and work in the cinema field. One shared experience among most of the women in our database was their fight against the roles imposed on them. Their struggle for emancipation indicates that, for all of them, there in fact was a clear definition of what it meant to be a woman, a category they often referred to and described in their works. Given the cultural environment they were raised in, they were told that their primary duty as women was to marry and care for the other members of their families. In one way or another, most of the women we have data on fought against this imposition. The crux of their struggle involved avoiding the reproductive labour they were destined for, resulting in social rejection. Nevertheless, the very privileged environment they were part of helped these women boost their productivity in the artistic and cultural fields as a direct consequence of their rejection (Clariana-Rodagut and Roig-Sanz). In this sense, these women could occupy social positions that were previously barred from women, thanks to their newfound freedom.

Comparing men and women is useful as long as we use it to describe the differences themselves and their implications, but comparison proves useless if we assume that men and women operated under equal conditions and thus expect equal, or even comparable, results. In no way can the public roles of the women at hand be approached using the same methods we would use to analyse men’s contributions within the same context. Although men’s contributions have always been historically acknowledged, women’s contributions did not receive the same praise because of their limited access to the public sphere. Therefore, if women’s contributions were to be compared to their male counterparts’ using the same tools of measurement, women would always appear less relevant – or gifted – in the history of culture. The above reasoning has led us to work on certain compensation strategies which, taken together, make up the method of analysis we propose in this paper.

Our analysis leverages our research group’s data, which is stored in the Nodegoat Virtual Research Environment. The database has been filled and enriched since 2019 and, therefore, is the result of the collaboration between current and past members of our research group. We believe it is important to be clear about the work involved in compiling data, given that such labour is often invisibilized, as noted by Catherine D’Ignazio and Lauren Klein in their book Data Feminism (2019). This invisibility implies that the places from which datasets are built are rarely acknowledged. Thus, little responsibility tends to be assumed around the biases affecting data-collection, cleaning, and systematisation processes. While knowledge should always be situated (Haraway), it is imperative that we specify our places of enunciation as researchers. This means that we must take responsibility for our biases, recognize what is being left out and what we are taking for granted, and clarify where the data were gathered and what procedure was used to extract them. The goal of such a process is to avoid what Haraway calls “the god trick,” that is, the deployment of a perspective that is situated nowhere, presuming that what we are showing reflects reality, instead of reflecting just one of its parts, our version of it. We aim to steer clear of the idea that the data cannot lie.

Thus, it is worth mentioning that the data-compiling team operates from the south of the Global North and that the team’s salaries are paid by a prestigious funding institution, the European Research Council. The means available to conduct our research are greater than average. Most of the people who have taken part in gathering our data are cisgender and white, and come from middle-class backgrounds. The data are biassed by our own standpoint.

The project behind the data collection, “Social Networks of the Past: Mapping Hispanic and Lusophone Literary Modernity (1989-1959),” consists of four research lines. These lines investigate cultural assets in the Ibero-America of the first half of the twentieth century. We aim at finding the mediators who spearheaded the exchange of material (e.g., books) and inmaterial (e.g., ideas) goods, but also the circulation of people. We posit that these relationships and exchanges provide the best windows to understanding the contribution that Ibero-America made in the building of artistic modernity. Our main research objects are 1) translations in cultural and literary historical journals, 2) the International Committee of Intellectual Cooperation of the League of Nations, 3) film criticism in specialised and cultural historical journals, and 4) women of the first Ibero-American film clubs. The information we gathered involves human and non-human agents and agencies (Latour), and using the actor-network theory’s tenet “follow the actor,” all the agents found during our source’s harvesting phase were uploaded into our database to unveil the networks around our objects. Therefore, in our database, we can find data on associations (for example, linked to historical journals), cultural events, and people. We also have data on people with whom the participants of our main objects exchanged letters and who collaborated in books and articles published by our human actors. Given our data’s intrinsic heterogeneity, our sources are varied, as they come from primary sources and secondary bibliography. Primary sources can be located in personal, municipal, and national archives, or in historical publications, thus including correspondence, historical documents, pictures, exhibition catalogues, member lists, etc.2

One of the consequences of using heterogeneous sources is that we fed the records on the relationships based on participation in events and publications. Yet, we do not possess data on associations’ every event but, in most cases, we have data on just a few events organised by a subset of associations. Likewise, the information does not cover every actor at a specific event or association, but only those we managed to find in the sources we had access to. Most of the data in this paper has been manually gathered. During the data harvest and uploading, each member of the team added descriptors using well-established concepts from their own fields of expertise. For instance, we have included the descriptor “cinema” to describe a given event’s topic, the specialisation of a journal, or the main topic of a specific text. These descriptors are written in English – as this is our research group’s communication language – and in Spanish – the main vehicular language used by the agents and agencies we work with.

Data heterogeneity also affects the types of relationships we establish between actors. The plurality of sources and people who have extracted/curated the data may also lead to inconsistencies, which we have tried to mitigate through constant verification. The metadata available on each human actor can also vary. In this sense, the invisibilisation of women in cultural history, and the consequent paucity of data on them, is worth mentioning. Therefore, regarding the human actors, we have considered meta-information on gender, name (first and last), and professional or participation-related ties to magazines, events, publications, and institutions, when available. We have also included personal relationships for the most prominent people in our database, that is, the ones we have been extensively working on from a qualitative point of view.

To address the lack of data on women, we have created a category on relationships (either personal or professional) in our database. Although we do not always possess the empirical evidence (e.g., an article, photo, or list of collaborators) of the professional relationship between two agents, when working with women it is essential that we include the self-reflexive and personal texts in which these women declare their professional or personal relationship with certain relevant actors in the cultural or cinematographic fields.3 In our category “relationships,” we have selected only personal, romantic relationships (e.g., wife-husband, or unmarried couple) and those defined as professional relationships. Such a selection is based on our knowledge of the historical context. Our expertise, in fact, leads us to believe that women married to “prominent” men almost always worked in their shadows. See, for instance, the case of Lola Álvarez Bravo.4 Concerning professional relationships, we have traced them thanks to primary sources (e.g., self-reflexive texts) or other sources (e.g., photographs), providing empirical evidence of the existence of a relationship between two (or more) people.

In sum, the main criterion to select relevant data within the whole dataset stored in Nodegoat was that the agents had to have some relationship with the film field. This criterion entails the selection of those institutions (journals, film clubs, federations, etc.), events, and people that played a role or were related to the film field, with the latter having worked or somehow participated in any such events, institutions, and journals. Our decision is based upon the paucity of data and the need to work with indirect data. Thus, even if for some people we only have data related to a single event, we will still consider them actors tied to the film field. In the next section, we provide a detailed description of how each channel (or type) of interaction has been defined and subsequently used to generate a network of people.

In mathematical terms, a graph (or network) is an object made of a certain number of points (denoted by N) called nodes or vertices, connected by a given number of edges or links (whose number is equal to E) (Latora et al.). The number of connections of a node is called its degree, and we denote it as k. An isolated node is a node with no connections. We use the term path to indicate the sequence of edges (or nodes) that needs to be crossed to go from the path’s origin to its destination. A connected component of a graph is the set of mutually reachable nodes via a path. The biggest component in the graph is called its giant component (Latora et al.).

Below we present how each channel (i.e., type) of interaction was used to build the corresponding network. Each of these networks can be thought of either as an independent entity or, alternatively, as a subnetwork (i.e., a layer) of a multiplex/multilayer network (Bianconi).

One way of quantifying the marginalisation of women in the film field is to study their presence – and abundance – across the network’s k-shells, as outer ones tend to be populated mainly by nodes belonging to the network’s outskirts, whereas deeper k-shells are populated by more influential spreaders, which also tend to occupy central positions in the network. To this end, we have built a network – labelled as ALL – obtained by collapsing together all the layers (i.e., the REL, COR, CB, PUB, and EV networks) of the multiplex network. The collapse of distinct layers into a single network implies that the presence of an edge in the ALL network stems from its existence in, at least, one of the layers. Table 1 summarises the structural features of the ALL network. Upon inspection, we may highlight that the number of edges, E, and the size of the biggest connected component, S, are considerably larger than those of the single layers. These differences, combined with the smaller number of connected components, Ncomp, and isolated nodes, Nisol, tell us that the ALL network is considerably more cohesive than the layers’ networks when taken separately. With this picture in mind, we can study the k-core decomposition of the ALL network. Specifically, for each k-shell, we have counted its number of nodes classified either as men, women or “unknown”9 and divided such numbers by the total number of nodes in the k-shell.10

The left panel of Fig. 1 displays the fraction of women, men, and unknown people present in each k-shell of the empirical network. Notably, the women tend to concentrate in a few k-shells, especially in the outer – and shallower – ones. However, the same analysis performed on the randomised networks delivers a distinct picture. By glancing at the right panel of Fig. 1, we notice how women’s presence tends to be more widespread across all the k-shells. The fraction of women appearing in a k-shell appears more homogeneous in the random counterpart than in the empirical network. These features make us suspect that there could be some kind of “force” exacerbating women’s marginalisation by pushing them towards k-shells where their influence as cultural mediators is weaker.

The marginalisation of women within the k-core decomposition observed in the ALL network (displayed in Fig. 1) raises the question of whether such a phenomenon stems directly from a single – or a few – channels of interaction or is due to their interplay. It has been proved that the structural features of networks obtained by merging together the layers of a multiplex network can be quite different from those of their layers considered singularly (Cardillo et al.). To find whether marginalisation is directly related to the multilayered nature of our system, we leveraged the full structure of the multiplex network and repeated our analysis of each of the layers separately. Such a procedure allows us to understand how marginalisation affected women in different facets of their activities (i.e., channels of interaction). We hypothesise that each channel of interaction would show different levels of marginalisation depending on the type of interaction involved: more private channel of interaction (such as COR or REL) would display smaller levels of marginalisation among women, whereas a more public channel of interaction (e.g., EV) would reveal higher levels of marginalisation.

To quantify the overlap of information in each layer pair, we use Eq. (1) to compute the pairwise Jaccard index J(α,β) between layers 𝛼 and β. Figure 2 shows the heatmap matrix of the values of J computed for all the pairs of layers. Except for the elements of the main diagonal (comparing a layer with itself, thus translating to a value of J=1) all the remaining values of J are quite small. This means that the amount of redundant (i.e., overlapping) information encoded in our layer networks is quite small, ranging between 0.0 and 0.031. In other words, our layer networks are quite distinct from one another. Such differences justify the use of multiplex network formalism (Bianconi).

To delve into the source of women’s marginalisation, for each of the layer networks, including the ALL one, we computed the difference between the empirical fraction of women belonging to a given k-shell, fWe, and the same quantity computed in the randomised networks, fWr (see Figure 3). Specifically, for each network G we computed the difference ΔfW=fWe−fWr for each k-shell available. A positive difference (i.e., ΔfW>0) indicates that for that k-shell, there are (proportionately) more women in the empirical network than in the random counterpart. Conversely, a negative difference (i.e., ΔfW<0) indicates that – on average – the random counterpart has proportionately more women than the empirical one. Finally, a difference equal to zero (i.e., ΔfW=0) indicates that the fraction of women in that k-shell is the same in both the empirical and the random networks.

By inspecting Figure 3, we may highlight a few remarkable features. First, random networks tend to have a shallower k-shell structure than their empirical counterparts. The red star indicates the position of the value of the degeneracy for random networks, Dr. We notice how for all networks (except the COR one), Dr is smaller than in the empirical case (see also Table 1). Such a recoil has been observed already in empirical and artificial networks (Malvestio et al.) and is due to the shattering of tightly knit groups of nodes (e.g., communities) operated by the random rewiring mechanism. Another feature of Fig. 3 is that marginalisation occurs mainly in the EV layer but also in the PUB and CB layers. In contrast, it is absent in those layers corresponding to “private” interaction channels, such as REL or COR. These opposite behaviours originate from the intrinsic nature of the EV, PUB, and CB networks, which proceed from the projection of two-mode networks (i.e., networks with two types of nodes with connections existing only between nodes of different type) into single-mode networks rich with cliques (i.e., groups of nodes all connected with each other). This dichotomy suggests that public interactions were more prone to marginalise women than those occurring in the private sphere. This is why it is crucial that we compare the single-mode networks’ results (COR and REL) to those of two-mode networks (EV, PUC, and CB) and give them the same value when analysing women’s contributions to the cultural field. Finally, we observe how the collapse of the available interactions’ channels into a single layer gives rise to a less defined marginalisation scenario than when the layers’ networks are studied independently.