Social Studies of Science最新文献

The contemporary prevalence of artificial intelligence and machine learning methods has resulted in a rich literature on the factors that shape computational research. This article draws on the laboratory studies literature to examine how platforms' socio-technical infrastructures shape contemporary computational social science research. Based on 18 months of online ethnography of a university laboratory and 15 in-depth interviews with its researchers, the article makes two main arguments. First, for computational social sciences, platforms function as laboratories where the social is selectively carved and transformed, to make it knowable with computational methods. Thus, it makes the case that platforms manufacture the objects of analysis in computational social research and provide the social as a domain. Second, because of the significance of social media platforms as data laboratories for computational research, in contrast to the claims of data sciences to be domainless, these sciences may derive some of their epistemological and occupational power, as well as their cultural authority, from digital capitalism.

In this article, the authors identify the disciplines that have taken an interest in masks over time, as well as how, in what proportions, according to what concerns, with what developments, and possibly with what effects. They ask whether the multiplicity of disciplinary perspectives is likely to lead to the emergence and sharing of new concerns, especially environmental ones, or whether the balkanization and juxtaposition of disciplines may leave certain aspects in the dark and thus contribute to the persistent production of a certain kind of ignorance. Based on a bibliometric and textometric study of more than 6000 scientific articles (1892-2023), they show the extent to which the Covid-19 pandemic has turned the study of masks upside down. It has encouraged the development of multidisciplinary and even interdisciplinary approaches, even if the legacy of almost exclusively medical sciences and engineering tends to severely limit hybridizations. The study highlights the possible emergence of a new movement of 'scientization of the popular', which leads scientists to incorporate the everyday concerns of ordinary citizens into the conduct of their research, thus challenging and reversing the well-known process of popularizing science.

Science and Technology Studies (STS) has long been criticized for eroding science's authority and blurring the line between opinions and facts, and more recently for contributing to the emergence of 'far-right populists' and 'anti-science movements'. This article argues that 'post-truth politics' does not necessarily entail epistemic democratization. This claim is based on an investigation of the controversies surrounding public health policies during the Covid-19 pandemic in Brazil. In 2021, the Brazilian parliament established an inquiry into allegations that President Jair Bolsonaro neglected expert advice and actively promoted contagion, causing a surge in hospitalizations and deaths. The analysis of testimonies and ensuing debates suggests that so-called 'science deniers' did not contest scientific authority but instead positioned themselves as critical thinkers who sought to expose political interests masquerading as facts. Bolsonaro's allies claimed to be supported by unbiased experts who had more prestige and credibility than those cited by the opposition. In short, they were not against modern scientific knowledge and methods but claimed to speak in the name of the best available scientific evidence. Thus, instead of blaming STS for the 'post-truth era', we should further engage with its conceptual tools to understand the complex relations of 'far-right politics' and scientific institutions. More specifically, we need to investigate how expertise gets distributed, how different statements accumulate authority, and how scientific knowledge is enacted across multiple fields of practice.

Cyber threat intelligence firms play a powerful role in producing knowledge, uncertainty, and ignorance about threats to organizations and governments globally. Drawing on historical and ethnographic methods, we show how cyber threat intelligence analysts navigate distinctive types of uncertainty as they transform digital traces into marketable products and services. We make two related contributions and arguments. First, building on STS research on uncertainty and ignorance, we articulate two kinds of uncertainty and their potential to interact. Coordinative uncertainty emerges from socially and technologically distributed processes of producing, interpreting, and reporting data that emerges when analysts create standards to make data travel. However, standards can be exploited by intelligent adversaries behaving in deliberately unpredictable ways. We argue that efforts to reduce coordinative uncertainty through standardization can thus ironically increase opportunities for adversarial uncertainty, creating a potential tradeoff. Second, we aim to show how STS can deepen and integrate studies of international security and political economy, by providing an example of how the geopolitical structuring of private industry shapes the science and technology that industry produces. In particular, we argue that the political economy of the cyber threat intelligence industry tends to produce relatively little knowledge about cyber operations that are conducted by governments in the U.S. and its allies, and more about cyber operations conducted by adversaries of U.S. and allied governments. We conclude with a reflection on the broader significance of these findings for the ways that coordinative and adversarial uncertainties refract through the political economies of technoscience.