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A psychological phenomenon termed "stereotype threat" has been shown to contribute to women's underperformance and underrepresentation in math and science fields. Within the virtual reality literature, a recent study utilized gendered body-swap illusions (i.e., women in male virtual bodies) to mitigate the effects of stereotype threat among a sample of female participants. The present research provides a much needed replication of this intervention, as well as a critical extension of virtual reality research on the Proteus Effect to test whether stereotype threat can be induced among male participants immersed in a female virtual body. Results supported both the replication and extension hypotheses; female participants embodied in male avatars were buffered from stereotype threat whereas male participants embodied in female avatars suffered from stereotype threat. Avatar gender also influenced participants' math confidence and awareness of the negative societal stereotype regarding women's math ability.
Online learning environments eliminate geographical barriers and enable new forms of collaboration between students at large scale. Self-presentation within such environments affects how students interact with learning content and with each other. We explore how anonymity/identifiability in user profile design impacts student interactions in a large multicultural classroom across two geographical locations. After triangulating 150,000 online interactions with questionnaires and focus groups, we provide three major findings. First, being identifiable had a significant impact on how students accessed and rated content created by their peers. Second, when identifiable, cultural differences became more prominent, leading some students to avoid content created by classmates of certain nationalities. Finally, when students interacted with their real identities, there were significant and negative gender effects which were absent when students were anonymous. These findings contribute to our understanding of social dynamics within multicultural learning environments, and raise practical implications for tool design.
We study how the ratings people receive on online labor platforms are influenced by their performance, gender, their rater's gender, and displayed ratings from other raters. We conducted a deception study in which participants collaborated on a task with a pair of simulated workers, who varied in gender and performance level, and then rated their performance. When the performance of paired workers was similar, low-performing females were rated lower than their male counterparts. Where there was a clear performance difference between paired workers, low-performing females were preferred over a similarly-performing male peer. Furthermore, displaying an average rating from other raters made ratings more extreme, resulting in high performing workers receiving significantly higher ratings and low performers lower ratings compared to when average ratings were absent. This work contributes an empirical understanding of when biases in ratings manifest, and offers recommendations for how online work platforms can counter these biases.
Harassment is a persistent problem in contemporary online environments, with women disproportionately experiencing its most severe forms. While critical scholars posit that online gender harassment may be linked to men's anxieties about fulfilling normative masculine gender roles, this relationship has not been examined by empirical research. We survey 264 young men between the ages of 18-24 about their masculinity anxieties and their perceptions of harassment directed at a woman on Twitter. We find that men who perceive themselves as less masculine than average men report higher endorsement of harassment. Further, we find that the relationship between masculinity anxieties and harassment endorsement is mediated by men's adherence to masculine norms and toxic disinhibition. We interpret these results through the lens of social media's specific affordances, and we discuss their implications for technology designers and other practitioners who wish to better detect, prevent, and remediate online harassment by accounting for the role of gender.