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The emergence of COVID-19 in early 2020 led to a flood of numbers in public discourse, from the ever-updating count of daily cases of coronavirus to the fabled R-value. But this vast sea of statistics, data, league tables, indexes, metrics and indicators often led to more confusion than clarity. Therefore, we need to find better ways to think through these number-rich contexts - especially as we enter into the ‘post-pandemic’ era. To do so, this book puts forward the Life of a Number methodological approach. Seven particularly important numbers from the pandemic were analysed through a five-stage process that paid attention to demand, production, communication, public meaning and backgrounding. The narrative that emerges holds significance for COVID-19 but, more broadly, it forms the theoretical framework of data bounds. This concept helps scholars think through how the quantitative becomes a meaningful way to engage with, think about, discuss and change certain phenomena. Each chapter provides a distinct perspective on data bounds, with the conclusion putting forward a four-point toolkit to help scholars put data bounds into practice. It calls for academics to pay attention to media and communication, interrogate and appreciate quantitative realism, examine how data bounds can maintain or challenge power, and determine why some data bounds dominate. The methodological, empirical and theoretical contribution of this book is one that bridges the gap between Critical Data Studies and Media and Communication, providing a starting point for scholars looking to think through the ever-expanding net of data in the ‘post-pandemic’ world.
Queering science communication should give LGBTIQA+ people power and the chance to play. This conclusion summarizes the various contributions to this book and draws out some unresolved issues that deserve attention in future research and practical science communication endeavours.
Queering science communication must entail something more than recognizing LGBTIQA+ people are present in our discipline. The field must review underlying structures and values in our culture and together work out how to improve. Science communicators must expand the way we understand and discuss the diversity of humanity and then embrace these ways of being within our research and applied practices.
The final chapter brings together the lessons of the six empirical chapters to put forward a four-part toolkit to help scholars put data bounds into practice. First, scholars must pay attention to media and communication by examining media ecosystems. Second, they must both interrogate and appreciate the power of quantitative realism. Third, academics need to examine how data bounds can maintain or challenge power. Finally, and the hardest task of all, those interested in taking this approach must attempt to determine why some data bounds dominate over others. The chapter concludes by asking ‘Is there any hope?’, with the answer pointing to the role of social and political movements rather than politicians, journalists and experts.
The shape and scope of data bounds are not just determined by quantitative realism, policy, identity construction or the data itself. Data bounds emerge into – and so are shaped by – historical norms. This chapter examines how this occurred through the projection of ‘90,000 cases per day’ by 19 July 2021 by Christina Pagel. This projection failed to ‘take off’ like other figures during the pandemic. While some would point to its simplicity (and the way it ultimately proved to be wide of the mark), this chapter argues that it did not circulate because of the way inequality had been normalized in the UK. This historical context meant that Trade-Off did not factor inequality into the equation of risk – the effect on the most deprived in society was considered acceptable collateral for the freedoms of those less deprived. This was the reason that Pagel’s projection was ignored: 90,000 cases offered very little threat to certain groups in society and a considerable threat to others. Such a case study emphasizes the need to understand and interrogate the contingencies into which data bounds emerge.
When a book focuses on the quantitative, it can often emphasize the way people use numbers to understand, rationalize or compute. But the quantitative is emotive too. This chapter focuses on the now iconic data visualization from the pandemic: the peak and troughed graph of daily cases, hospitalizations and deaths. Specifically, it examines the way the graph was presented by a Sky News journalist – and how this performance flouted the convention of presenting these types of graphs about death. Using genre studies analysis, it argues that the data visualization was imbued with the emotional experience of those living through Trade-Off. One that can be defined by both uncertainty – the rising and falling of cases – but also of large-scale tragedy – the thousands of people who died per day. The journalists’ animated performance lacked the sobriety that the presentation of such a culturally important graph needed. In doing so, it pointed to the emotion with which we often imbue data visualizations.
The opening empirical chapter outlines Trade-Off: the dominant data bound of the pandemic that positioned economic indicators and health metrics in direct opposition to each other. As GDP growth, unemployment and trade improved, cases, hospitalizations and deaths declined – and vice versa. The chapter argues that Trade-Off dominated because of the government’s response to coronavirus. In adopting a mitigation strategy, they engaged in a constant trade-off: they would introduce national lockdowns when health metrics worsened – even if this affected the economy badly – and then open up society when health metrics were suitably low – allowing the economy to grow again. This stood in direct opposition to other countries, mainly in East Asia and Australasia, that took an elimination or containment approach. It was here that an alternative data bound emerged: Protect Both. In these countries, the prevailing logic was that over the long term you either protected both the economy and health or saw them both decline. The message from this chapter is clear: policy structured the data bound that emerged.
Quantitative realism does underpin data bounds. But the desire for data bounds to be established can often underpin a strategic need for quantitative realism in the first place. In other words, the tail can wag the dog. This chapter explores this idea by tracing the life of two figures concerning people’s belief in misinformation: how The Plandemic was viewed ‘more than eight million times’ and how ‘7% of Britons think there is no hard evidence that coronavirus exists’. Both figures point to a key take-home message: using simplistic quantitative data to make definitive conclusions about people’s beliefs in misinformation can be read as a strategic ploy by the news media. The chapter argues that journalists ignored the questionable empirical grounds of misinformation because they needed to emphasize its threat to construct their own identity as trustworthy gatekeepers of information. In other words, the news media needed the data bound of misinformation to be underpinned by quantitative realism.
Citizen and community science has been recognized as an impactful development in science communication and has become an important avenue for potential lifelong STEM engagement. This chapter focuses on LGBTIQA+ people’s experiences participating in citizen science projects in the US with recommendations for improving their experiences and broadening participation. The chapter highlights how few community and citizen science projects have focused on queer topics, issues, or participants in the US and elsewhere.
Despite this notable effort to meaningfully support inclusion among some axes of identity, a remaining challenge is creating science communication communities that are radically and intentionally inclusive of queerness.
This chapter seeks to present how this came to be and provide recommendations for how we might build science communication communities that are inclusive of intersectional queer identities moving forward. It discusses practical strategies for building inclusive networks and events, emphasizing the need to continue adapting to diverse people and needs.
Queer people face specific issues in the world of science and have unique contributions to make to science communication, so bringing ‘queer’ and ‘science communication’ together in dedicated events, products, and networks is an important part of queer protest, liberation, and visibility. Yet an examination of the science communication research literature reveals that little has been published on the intersection between queerness and science communication over the past thirty years or more.
This introduction provides the foundations of a book on queering science communication. It covers the structure of the book and describes how contributors approached queering science communication in different domains.
