Matthew Hall

This article explores the contribution agent-based modeling (ABM) can make to the study of LGBTQ workplace inequalities and, conversely, how ABM can adapt to theoretical traditions integral to LGBTQ studies. It introduces an example LGBTQ workplace model, developed as part of the CILIA-LGBTQI+ project, to illustrate how ABM complements existing methods, can address methodological binarism and bridge macro and micro accounts within LGBTQ studies of the workplace. The model is intended as an important starting point in developing the role of ABM in LGBTQ research and for bridging qualitative- and quantitative-derived insights. Likewise, the article discusses some approaches for negotiating theoretical and methodological tensions identified when integrating queer and intersectional insight with ABM.

Queer Theory is a radically deconstructionist perspective within the humanities and social sciences. Since its initial emergence in the late 1980s and early 1990s in the field of sexualities studies, Queer Theory has increasingly been used to challenges normative notions of self, identity, temporality and the nature of being, more broadly. Whilst Queer Theory has been utilized, to some extent, in gerontology and aging studies, this article makes an original contribution to this endeavor, assessing the potentiality and problems with queer(y)ing three aspects of aging: chronology; cognition; and frailty and vulnerability. To achieve this, the article draws on ideas from some key Queer theorical writers, existing studies of queer aging and illustrates theoretical points with qualitative data collected from two LGBTQ+ projects to illustrate. The article also considers problems with Queer Theory in challenging normativities associated with aging. It is concluded that despite problems, Queer Theory remains an important and valuable theoretical approach for disturbing and challenging many of the norms and understandings that shape and constrain older LGBTQ+ people's lives, in particular, and therefore have importance for how we think and understand aging and later life sociologically.

The outbreak of COVID-19 has transformed societies across the world as governments tackle the health, economic and social costs of the pandemic. It has also raised concerns about the spread of hateful language and prejudice online, especially hostility directed against East Asia. In this paper we report on the creation of a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class. The classifier achieves an F1 score of 0.83 across all four classes. We provide our final model (coded in Python), as well as a new 20,000 tweet training dataset used to make the classifier, two analyses of hashtags associated with East Asian prejudice and the annotation codebook. The classifier can be implemented by other researchers, assisting with both online content moderation processes and further research into the dynamics, prevalence and impact of East Asian prejudice online during this global pandemic.