Gender in Play: Mapping a Girls' Gaming Club

This paper, by Nicholas Taylor, Jennifer Jenson, and Suzanne de Castell, examines how girls' access to gaming is shaped by material and discursive conditions that render their participation tenuous, even within spaces ostensibly dedicated to them. Drawing on audio-visual data from the first phase of the Education, Gender and Gaming (EGG) project, a school-based study conducted in a Greater Toronto Area elementary school, the authors analyze a 30-second clip in which two boys interrupt a girls-only gaming session. The authors argue that conventional textual ethnographic methods flatten the multimodal communication involved in such interactions, and they introduce the MAP (Multimodal Analysis Program) tool to chart gesture, posture, speech, and proximity as coordinated "semiotic scores."

The analysis finds that the girls respond to the boys' intrusion with immobility, silence, and, in the case of one girl, a tense request for the boys to leave, while another girl's gesture toward the console effectively invites the boys back into control. The authors read these reactions alongside survey data showing that female participants typically describe their gaming as spectatorship or play with male relatives, and alongside a pilot observational study of a university campus arcade, where male and female patrons differed in how they regarded spectatorship as legitimate participation. The authors interpret the episode as a performance of a gendered order in which boys naturally own and control gaming technologies, a pattern they link to Butler's concept of performative gender.

The study is explicitly limited in scope. It draws on a single clip from one school, with participants almost entirely self-identifying as southeast Asian, which precludes comparison across racial or cultural backgrounds. The authors position the work as a methodological and conceptual contribution, arguing that understanding girls' marginalization requires attention to embodied, multimodal interaction rather than textual preference data alone.