University of Technology, Sydney
University of Technology, Sydney
Royal Melbourne Institute of Technology (RMIT)
University of Technology, Sydney
Statistics describing the inequitable conditions for women in global film industries have been gathered and circulated for more than 30 years. These statistics have barely deviated despite the development and application of a range of equity policies. In some instances the participation of women has become marginally worse. Furthermore, the repeated release of poor equity data has given the industry’s structural misogyny an air of inevitability. This situation is not unique to the film industry.
Our project uses newly available forms of data and data analysis to proose innovative strategies for redressing the systemic and frequently personal bias against women in two different “merit based” industries – the film industry and academic grants schemes. Using data derived from the Australian, Swedish and German film industries as well as data from two different Australian research grant schemes, we propose, compare and evaluate several approaches to controlling collaborative network evolution in order to increase network openness. Our approach is informed by the findings of a major longitudinal study which found that “female actors have a higher risk of career failure than do their male colleagues when affiliated in cohesive networks, but women have better survival chances when embedded in open, diverse structures.” (Lutter 2015)
This project rests on two inter-related manoeuvres then. Firstly, it flips the object of analysis. If we are going to make these industries a better place for women and other minorities then we need to understand the specific operations of gatekeeping that maintain the dominance of white, cis men. The second aspect of the project is to use the data we have collected about specific collaboration networks to propose an innovative course of action to change male dominated, exclusionary environments.
This data, on creative roles in films and on researchers receiving grants, contains not only information about the characteristics of projects and all the people involved but also, equally importantly, relational data that enables us to look into the connections within and across teams working on films or research projects respectively. Social network analysis (SNA) provides methods for visualising these group relationships, and through quantitative measures that characterise network structure, provides methods for identifying strategically important components and participants in the network. It also therefore points to ways in which these networks can be most effectively “dismantled” or opened up.
Network visualizations are useful for observing the implicit structure in the collaboration data, for understanding the scale of the problem and for identifying the key connected players. By adding the dimension of gender to these network visualizations we can clearly see the influence of gender on patterns of domination. In addition to making the existing network patterns visible our further concern was to see beyond these patterns and look for ways in which the data could suggest the most effective interventions for challenging and changing the status quo.
In this regard, network visualizations enable us to quickly identify outliers, and easily demonstrate the discrepancies between a given network and the more open reference network we would like to achieve. By depicting changes in both the network structure and its components, visualizations can facilitate the process of testing different policy proposals for achieving social change in organisational or industrial settings and can be used to monitor the emergence of new patterns (especially unwanted ones).
There is some precedent in approaching network visualizations in this way. Crime experts and counter terrorist specialists have used “criminal network analysis” for example to identify opportunities to undermine the coherence of dominant groups.
Drawing on the literature on the use of social network analysis to characterize criminal networks and identify key nodes whose removal would disrupt the network (i.e., Borgatti, 2006; Rostami & Mondani, 2015; Schwartz & Rouselle, 2009; Réka A., Hawoong J., & Barabási A-L., 2000), we investigated the network of male-only producers and other creatives in the film industry and male-only networks of researchers in the university sector. We investigated the impact of key players in these networks, and the hypothetical impact of removing different key players.
Specifically, we used Borgatti’s network fragmentation factor (F) (equation 4 in Borgatti, 2006) as a quantitative measure of network disruption. In this equation, the F value is 0 when there is no fragmentation in a network (all nodes connected in a single component), and is 1 when all nodes in a network are isolated. Using an iterative script, F was calculated for the initial network, a node was removed, and then F was recalculated to assess the impact of the node removal on network fragmentation.
At each iteration, the increase in F obtained from removing a range of male producer or male researcher nodes from the initial networks was calculated and compared. The large(est) male producer/researcher node in the centre of a given network suggests itself as a node whose removal would significantly increase the network fragmentation, and it was indeed the case that this change yielded the largest increase in network fragmentation. Those male producer or researcher nodes whose removal from the initial network yielded relatively large increases in network fragmentation were also observed to have relatively high values of ‘betweenness centrality’, as computed for the initial network. Node betweenness centrality measures how often a node appears on shortest paths between nodes in the network. A high betweenness centrality in the initial network provides a heuristic for identifying candidate nodes for removal that would significantly increase the network fragmentation.
This paper will present the project’s findings on the best strategies for dismantling domination patterns and behaviours in collaborative networks, one of them being removing the nodes with the highest betweenness centrality, or in the case of male dominated collaboration networks, removal of the men we call the “gender offenders”.
Borgatti, S. P. (2006). Identifying sets of key players in a social network.
Computational & Mathematical Organization Theory, 12(1), 21-34. doi:10.1007/s10588-006-7084-x
Lutter M., (2015). Do Women Suffer from Network Closure? The Moderating Effect of Social Capital on
Gender Inequality in a Project-Based Labor Market, 1929 to 2010.
American Sociological Review Vol. 80(2) 329–358 doi: 10.1177/0003122414568788
Réka A., Hawoong J., & Barabási A-L. (2000). Error and attack tolerance of complex networks.
Nature 406, 378–382, doi:10.1038/35019019
Rostami, A., & Mondani, H. (2015). The Complexity of Crime Network Data: A Case Study of Its Consequences for Crime Control and the Study of Networks.
PLOS ONE, 10(3), e0119309. doi:10.1371/journal.pone.0119309
Schwartz, D. M., & Rouselle, T. (2009). Using social network analysis to target criminal networks.
Trends in Organized Crime, 12(2), 188-207. doi:10.1007/s12117-008-9046-9
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Hosted at El Colegio de México, Universidad Nacional Autónoma de México (UNAM) (National Autonomous University of Mexico)
Mexico City, Mexico
June 26, 2018 - June 29, 2018
340 works by 859 authors indexed
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