When: 2022-06-29 10:40-12:20 (Tallinn time)
Where: University of Helsinki, Finland
Abstract – CUDAN Senior Research Fellow Vejune Zemaityte will give a talk co-authored with other CUDAN Senior Research Fellows Mila Oiva, Ksenia Mukhina, and Andres Karjus, and CUDAN ERA Chair Maximilian Schich at the ICA Regional Conference 2022: Computational Communication Research In Central and Eastern Europe.
Tracing gender diversity in labour networks of Soviet newsreel production 1945-92
Gender inequality in labour relationships is a prevailing concern within audiovisual communication industries across the world. Studies have repeatedly shown that most creative occupations in today’s screen media production remain to be male-dominated. Little scholarly attention has, however, been paid to the historical accounts of the gendered creative labour relationships beyond Western countries. This paper takes a cultural data analytics approach to examine gender diversity within the labour networks of the Soviet newsreel production industry.
The news media production industry in the USSR serves as an interesting case for analysing the gendered dynamics of creative labour because the Soviet doctrine proclaimed gender differences to be socially irrelevant, and the USSR featured high female labour force participation across different industry sectors compared to other countries at the time.
One of the main audiovisual means of communicating news to the wider public during the Soviet period were newsreels: short news clips displayed in cinemas before films. While the accuracy of news depicted in Soviet newsreels is questionable due to heavy censorship enforced under the Soviet regime, the creative networks surrounding the newsreel production nonetheless yield a unique case for the analysis of gendered labour dynamics in the production of audiovisual news media in a non-Western setting.
We analyze detailed metadata on 1,747 clips produced as part of the Daily News newsreel series during 1945-1992, including information about 1,623 individuals listed as directors, cinematographers, text editors, and other crew. The metadata was scraped from the newsreel archive digitized by the Net-Film company (www.net-film.ru/en/), which features over 9,600 clips across 77 newsreel series produced by various Soviet film studios.
Our approach combines the perspectives of communication and creative industry disciplines with network science and cultural history. We explore the gendered labour networks of Soviet newsreel production by investigating undirected unipartite projections which depict crew-to-crew collaboration relationships, as well as directed unipartite projections that illustrate director-to-crew hiring networks, with the added gender dimension of all crew members. The network approach enables us to explore the relational structures underlying the historical organization of creative labour within this audiovisual production industry.
Preliminary results indicate a high prominence of women audiovisual media creators across the creative leadership roles in the production of Soviet newsreels. Not only do we see the directorial work being split equitably between men and women during the time, but also observe long-lasting careers led by women directors. Moreover, women appear to be very well-embedded within the production network structures as characterised by high degree centrality of women directors. Nonetheless, stark gender inequality known from other audiovisual media production industries persists at the occupational levels of lower prestige such as that of a cinematographer where the work is split 9:1 between men and women.
The recent emergence of detailed, longitudinal datasets describing the dynamics of audiovisual communication industries situated beyond the Western world offers a unique opportunity for large-scale computational analyses of these previously understudied sectors with gender diversity in creative labour serving just one of many possible topics for investigation.
Keywords Gender diversity, cultural labour, newsreels, Soviet Union, cultural data analytics, network science