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With the COVID-19 pandemic introducing social distancing measures around the world, how we conceptualise and experience intimacy has significantly and suddenly shifted. Intimate moments such as funerals, weddings and the nurturing of everyday relationships have unfolded over video calls, and digitally mediated contact has been granted, for many, greater importance. At the same time, who we can be close to, and the conditions of this closeness have come under intense scrutiny as we become aware of how bodily proximity and bodily performances such as breathing are implicated in the spread of the virus. With this awareness comes a renewed intimacy with seemingly mundane bodily gestures and performances such as breath – and with the ways in which we are always entangled with those around us. In this chapter, we examine intimacy in a post-COVID future through the themes of proximity, breath and mediation. While intimacy is often conceptualised as occurring only between human subjects, we contribute to a more expansive understanding of intimacy that can account for the closeness and familiarity we feel with non-human objects. We argue that our social worlds are layered with familiar objects that facilitate our everyday encounters – a facemask or Zoom interface – and we argue that conceptualising intimacy must account for these entanglements.

In this chapter, educational philosopher Michael Peters discusses the emergence of new movements in thought and educational research practice in an “epoch of digital reason” that encompass the posthuman and decentered intimate scholarship. Peters describes changes that have occurred at the juncture of philosophy, culture, and science, probing the notion of a “coming after” of postmodernism in a post-truth era that has seen a rise in reactionary, anti-intellectual, anti-immigrant reaction across the Western world. Peters provides insight regarding this collection of changes in thinking, to which the decentering of subjectivity is critical, and even, as he suggests, one of the foundations of modern philosophy after Descartes. This shift in thinking across disciplines entails a turn to systems and ecological thinking; an understanding of consciousness as situated, distributed, and enacted; and a view of the world as constituted by productive difference. Other changes include connecting affect and cultural dimensions to research, which is expanding our view of science and what shapes science. Peters notes that these shifts turn us to new questions about rethinking concepts that are grounded in the liberal, intentional notion of the subject, such as agency and responsibility for one’s actions. As we engage in this rethinking, Peters suggests that we learn from indigenous studies, as indigenous peoples have been putting to work different forms of posthumanism for millennia.

This chapter outlines several approaches that researchers can use to help them conduct research that respects and includes autistic participants. It begins by highlighting some of the factors that should be considered before, during and after a study is conducted. It then explains some of the benefits and drawbacks of harvesting data from social media. It then concludes with a set of recommendations about inclusive research practices that can help autistic participants be equals in the research process.

The original contribution that this chapter gives to the field of autism spectrum research is to provide researchers with a clear and comprehensive outline of how to conduct research that is inclusive and respectful of autistic participants.

Social justice, true social justice, must be sought and obtained. Once obtained institutionally, social justice is manifested by a desire to maintain equality in otherwise diverse environments. The irony of social justice is that historically and currently, it is a societal ideal that is difficult to achieve. The uniqueness of every human being should be recognized in institutions that emphasize social justice, as being a strength. However, being that the nature of human beings is to be comfortable with fellow human beings who act and look similar to themselves, until made aware of our own hidden biases, social justice cannot prevail.

Most traditional research on work groups has studied groups and teams that are homogeneous with respect to culture. To alleviate the dearth of material on culturally heterogeneous teams, this chapter provides an overview of the impact of cultural diversity on groups and teams in today’s workforce. First, we focus on the problems involved in defining the constructs of “teams” and “culture.” Second, we provide a brief review of the cultural factors that have been identified as affecting human performance. This review serves as the basis for the third section of this chapter, which investigates if – and how – cultural heterogeneity affects team performance. Finally, we conclude with how culturally diverse workplaces can be managed and how to improve performance when faced with cultural diversity.

This chapter offers a critical evaluation of the disability scholarship of Michael Oliver, a leading developer of the British social model of disability, and Peter Singer, a philosopher whose utilitarian ethics excludes some persons with intellectual disabilities from full moral status. Through a critique of the simplified accounts of disability employed by these two very different scholars, this chapter explores the ontology and the moral significance of disability. The importance of the ontology of disability in relation to inclusion is also discussed.

We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single-factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state-space approach of Kim and Nelson (1998) and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets from Cesa-Bianchi, Cespedes, and Rebucci (2015) and interpret the empirical results in light of the Monte Carlo results.