The Artificial Intelligence Explanatory Trade-Off on the Logic of Discovery in Chemistry

Explanation is a foundational goal in the exact sciences. Besides the contemporary considerations on ‘description', ‘classification', and ‘prediction', we often see these terms in thriving applications of artificial intelligence (AI) in chemistry hypothesis generation. Going beyond describing ‘things in the world', these applications can make accurate numerical property calculations from theoretical or topological descriptors. This association makes an interesting case for a logic of discovery in chemistry: are these induction-led ventures showing a shift in how chemists can problematize research questions? In this article, I present a fresh perspective on the current context of discovery in chemistry. I argue how data-driven statistical predictions in chemistry can be explained as a quasi-logical process for generating chemical theories, beyond the classic examples of organic and theoretical chemistry. Through my position on formal models of scientific explanation, I demonstrate how the dawn of AI can provide novel insights into the explanatory power of scientific endeavors.

Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity

We model scientific theories as Bayesian networks. Nodes carry credences and function as representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to envisage and analyze the differential impact of evidence and credence change at different points within a single network and across different theoretical structures.

Perplexity as a provocation: revisiting the role of metaphor as a 'place holder' for the potential of COVID-19 antibodies.

This article revisits long-standing critiques of the role of metaphor in immunological discourse. Drawing on Alfred North Whitehead's speculative philosophy of organism, I focus on the use of metaphor to explain the process by which COVID-19 vaccine research is able to generate protective antibodies, the challenge of autoimmune disease and dengue fever antibodies. I suggest that metaphors are provoked by the perplexity that arises from presupposing that distinct morphological substances are the first order of reality. I conclude that rather than seeing metaphors as typically skewing conceptions of the body, as has been previously argued, those of memory, recognition and misrecognition may be instructive of a body in transition. Indeed, a process of transition that shows degrees of creativity. When gesturing towards the processual nature of infection and immunity, metaphors invite new modes of shared thinking across the disciplinary divide.

From cause and effect to causes and effects.

It is now-at least loosely-acknowledged that most health and clinical outcomes are influenced by different interacting causes. Surprisingly, medical research studies are nearly universally designed to study-usually in a binary way-the effect of a single cause. Recent experiences during the coronavirus disease 2019 pandemic brought to the forefront that most of our challenges in medicine and healthcare deal with systemic, that is, interdependent and interconnected problems. Understanding these problems defy simplistic dichotomous research methodologies. These insights demand a shift in our thinking from 'cause and effect' to 'causes and effects' since this transcends the classical way of Cartesian reductionist thinking. We require a shift to a 'causes and effects' frame so we can choose the research methodology that reflects the relationships between variables of interest-one-to-one, one-to-many, many-to-one or many-to-many. One-to-one (or cause and effect) relationships are amenable to the traditional randomized control trial design, while all others require systemic designs to understand 'causes and effects'. Researchers urgently need to re-evaluate their science models and embrace research designs that allow an exploration of the clinically obvious multiple 'causes and effects' on health and disease. Clinical examples highlight the application of various systemic research methodologies and demonstrate how 'causes and effects' explain the heterogeneity of clinical outcomes. This shift in scientific thinking will allow us to find the necessary personalized or precise clinical interventions that address the underlying reasons for the variability of clinical outcomes and will contribute to greater health equity.

Computational Scientific Discovery in Psychology.

Scientific discovery is a driving force for progress involving creative problem-solving processes to further our understanding of the world. The process of scientific discovery has historically been intensive and time-consuming; however, advances in computational power and algorithms have provided an efficient route to make new discoveries. Complex tools using artificial intelligence (AI) can efficiently analyze data as well as generate new hypotheses and theories. Along with AI becoming increasingly prevalent in our daily lives and the services we access, its application to different scientific domains is becoming more widespread. For example, AI has been used for the early detection of medical conditions, identifying treatments and vaccines (e.g., against COVID-19), and predicting protein structure. The application of AI in psychological science has started to become popular. AI can assist in new discoveries both as a tool that allows more freedom to scientists to generate new theories and by making creative discoveries autonomously. Conversely, psychological concepts such as heuristics have refined and improved artificial systems. With such powerful systems, however, there are key ethical and practical issues to consider. This article addresses the current and future directions of computational scientific discovery generally and its applications in psychological science more specifically.

Types of Uncertainty: Cities from a Post-pandemic Perspective

Almost all decisions occur under conditions of uncertainty. Understanding uncertainty is thus an essential prerequisite for effective decision-making. In this chapter, we started by recalling the classic distinction between probabilistic risk and severe uncertainty. We ground our analysis in Hansson’s recent classification of eight types of uncertainty: factual uncertainty, possibilistic uncertainty, metadoxastic uncertainty, agential uncertainty, interactive uncertainty, value uncertainty, structural uncertainty, and linguistic uncertainty. Based on this classification, we investigate and demarcate some of the determinants of each type of uncertainty by taking into account their different sources and scales. Finally, we apply our analysis of these determinants to urban decisions that might occur in a post-pandemic context and propose future lines of research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Collecting Middle-Class Memories? The Pandemic, Technology, and Crowdsourced Archives

Early in the COVID-19 pandemic, many groups initiated crowdsourced archives that invite members of the public to upload personal material connected to the pandemic. By archiving and publishing how "ordinary" people experienced and perceived the pandemic, these platforms influence how the pandemic is remembered and how historians will write about it. This article presents the example of covid-memory.lu and compares contributions to this platform from Luxembourg with others from German speaking countries. Analyzing users' thoughts about empty streets and skies and their experiences with computers and phones for work and leisure at home, this article discusses the potential and the limitations of crowdsourced archives for future historians of technology.

How the Pandemic Impacted Knowledge Production and Dissemination in India

The unprecedented spread of the COVID-19 pandemic compelled academic institutions and public repositories worldwide to come up with novel ideas in order to keep themselves functional. Adoption of new technology often constituted the fulcrum of such institutional responses. This article delves into some of the measures adopted by the Nehru Memorial Museum and Library, one of India's premier research institutes in social sciences. While doing so, it delineates a few anecdotal references from the past as well. The article emphasizes that the technological shifts in the wake of COVID-19 have far-reaching consequences so far as dissemination of knowledge through seminars and access to primary sources are concerned.

Navigating Mathematics Teacher Preparation During A Time of Crisis

In this paper we highlight the experience of a mathematics teacher educator (MTE) and their preservice teachers (PTs) in a middle school mathematics methods course during the 2020 shift to online instruction due to the COVID-19 pandemic. We believe it is valuable to report how the MTE reflected on their instructional decision-making in response to this massive transition to remote instruction. We also report that PTs needed support and guidance to employ new teaching practices they had learned in the methods course instead of reverting to familiar teaching methods.

What in the World Just Happened? Students' Self-Regulation in Asynchronous PreCalculus During the COVID-19 Pandemic

We describe students' learning practices in an online asynchronous PreCalculus course during Fall 2020, the first complete semester of distance learning induced by the COVID-19 pandemic. Results were compiled using a thematic analysis of a questionnaire administered to 43 students enrolled in PreCalculus at a university in the Midwest (United States). Students were given opportunities for active learning and various synchronous Q&A sessions, yet they primarily learned through watching videos and reading worked examples, minimizing interactions with the instructor and available tutors. The questionnaire results show that students knew active learning was helpful, but they were unable to curtail unproductive learning practices. The questionnaire also showed that students struggled to stay motivated and keep to a schedule. We conclude that by developing their study techniques and self-regulatory habits, students will be able to take more control over their learning, particularly in asynchronous classes.

Social Justice and Exponential Functions: Using Pandemic Data to Increase Student Understanding

This paper describes a class activity based on real data about COVID-19 death rates in California. The activity helps students learn about exponential func-tions while providing an opportunity to integrate social justice concerns into the mathematics classroom.

Mythological Endings: John Snow (1813-1858) and the History of American Epidemiology

During the COVID-19 epidemic, the name of the 19th-century English physician John Snow (1813-1858) has cropped up to a surprising extent, notably in connection with the severe cholera epidemic of 1854 in the district of Golden Square, London. It is repeatedly stated that Snow brought this epidemic of waterborne disease to an end by removing the handle of the Broad Street pump. It is also widely known that this story is a myth. Nonetheless, the Broad Street pump story as told by Snow's close friend Benjamin Ward Richardson remains embedded, partly, it is argued, because of its appeal to areas of the cultural consciousness. In America, Snow and his work on the epidemiology of cholera, including the Broad Street pump story, achieved a serious status which has endured, in one form or another, to the present day. In contrast to Britain, the heroic age of public health in America coincided with the optimism of the bacteriological revolution and higher hopes for medical science. However, this rapidly changing environment exacerbated differences of opinion as to what the small and emergent specialty of epidemiology should look like, what its project was, and where it should be based. Different versions of Snow's persona came to represent basic and often conflicting conceptions of epidemiology and the status (or lack of it) of its practitioners. For many, consciously or otherwise, the removal of the Broad Street pump handle remained an individualistic triumph, a single human intervention which resembled modern medicine in providing a "cure."

Paleosyndemics: A Bioarchaeological and Biosocial Approach to Study Infectious Diseases in the Past

Skeletons drawn from archaeological contexts provide a fund of data for assessing disease in general and timing of epidemics in particular in past societies. The bioarchaeological record presents an especially important perspective on timing of some of the world's most catastrophic diseases, such as leprosy, tuberculosis, plague (Black Death), and treponematosis. Application of new developments in paleogenomics and paleogenetics presents new opportunities to document ancient pathogens' DNA (for example, Black Death), track their history, and assess their beginning and end points. Paleopathological documentation of disease terminus is complex, in part owing to circumstances where past communities experienced overlapping epidemics, such as leprosy and plague. For most settings, these syndemics-whereby there is an interaction between two or more epidemic diseases-both exacerbate and enhance the burden of morbidity in a community or region. Fundamental to understanding the severity and duration of epidemics is the consideration of multiple factors that simultaneously influence the severity and duration of the specific infectious diseases in a community or region, including poor oral health, under-nutrition, iron deficiency anemia, and elevated parasite load. In our view, comprehending the beginning, the middle, and the end of epidemics requires understanding the wider context of syndemics, the multiple challenging circumstances that undermine health and community stability, and how biosocial factors differentially affect the immune competence of individuals. This article provides several examples of the application of bioarchaeology and syndemics theory in achieving an understanding of how epidemics end. Pathogens continue to circulate, even after what appears to be the end. In effect, then, there is no "end," just evolution of opportunistic pathogens and our ability (or not) to mitigate them.

Encountering Semiotic Misdirection in Covid-19 Etiquette Guides

This paper examines Global health misdirection unfolding at the semiotic level of Covid-19 related texts and images produced by the World Health Organisation. I argue that such public health materials, claiming neutrality and universal applicability, become multimodal etiquette guides that presume normal bodies and middle-class social environments. I give specific attention to how Covid-19-related materialities, affordances and emotive actants directly contribute to elite-making, stratification and strategic cultivation of shame and embarrassment with regard to Covid-19 etiquette. By tracing such an example of 'semiotic misdirection' in global health, I invite STS and adjacent communities to approach the circulation of public health materials as a semiotic practice that creates novel kinds of oddities and stratifications, and to consider the enactment of seemingly neutral and value-free public health rules as morally-charged etiquette.

Masked Societies in East Asia: A Forum on the SocioMaterial History of Face Masks Introduction

In the latest issue's "Editor's Note" of EASTS, Wen-hua Kuo made a call to East Asian science studies scholars to commit to an archeology of the social and technical infrastructure of epidemics. Coincidently, ten historians and sociologists working on science, technology, medicine, and environment with a focus on China, Japan, Taiwan, Singapore, and South Korea had just begun a collective effort to understand how face masks had become the most important part of the current pandemic governance in East Asia. As its first step, a virtual workshop, "The Socio-Material History of Masked Societies in East Asia," was held at the Max-Planck Institute for the History of Science on 26 October 2020. This forum aims to introduce the virtual meeting's outcome to the wider EASTS community and encourages them to engage with the collaborative enterprise to investigate the history of masks. All papers focus on the socio-material dimension of masks while problematizing current culturalist explanatory narratives about "masked societies" in East Asia. By doing so, the papers show how mask use is closely linked to heterogenous but interconnected entanglements of environmental governance, political movements, and risk cultures in East Asian polities. It interrogates these relationships in the context of scientific controversies and quarantine regimes.

The Two Virtues of Science

During the COVID-19 pandemic, there was disagreement over whether the science supported facemask mandates. This paper interrogates debates over this question, paying particular attention to an ambiguity between two scientific virtues: epistemic caution and epistemic responsiveness. I suggest that there is an argument from each virtue to reasons to trust scientists' claims in policy debate. However, as the case of facemask debates illustrates, it is not clear that scientists can possess both virtues simultaneously: the two virtues are in tension. After showing how this general framework can help us better understand debate, I turn to consider some possible ways of resolving this tension, arguing that none of them is entirely satisfactory.

Mistrust in Numbers: Regulatory Science, Trans-science and the Crisis of Expertise

This paper characterizes the crisis of expertise, especially as it manifested during the covid-19 pandemic, as a crisis of trust in regulatory science. The temporal structure of the facts produced by regulatory science differs from Kuhnian "normal science," while they also contain profound distributional implications. As a result, they suffer from a set of congenital problems that provoke mistrust in a way that normal science facts do not. While "expertise" is often offered as an answer to these problems, the paper shows that it is a symptom of the malaise, reflecting a situation where it is no longer clear how to decide between competing claims to authority as experts. The current mistrust in experts and regulatory science during the pandemic, therefore, is part of a longer and systemic crisis of expertise provoked and sustained by multiple factors. The paper then offers an unsystematic set of rules of method to observe when addressing the thorny issues involving trust and mistrust: 1) trust is not a subjective attitude that can be measured by a survey;2) mistrust is not the opposite of trust;3) trust is a social skill involving a set of ethnomethods for distinguishing between responsible and "blind" trust;4) attention to temporal framing is key to these methods;5) disruption of this temporal framing - as routinely happens with regulatory facts, and especially during the pandemic - destroys trust.

CITIZENSHIP AND SOCIAL TRANSFORMATION New discourses and action against climate change

Climate change has been at the centre of the environmental debate for three decades. Despite international agreements, humanity has not been able to stop the global increase in temperature. The coronavirus crisis has relegated the climate emergency debate to the background, although the connections between climate change and the pandemic prove that we have many reasons to act to mitigate climate change. Public policies have favoured regulations, market instruments, and modernisation strategies that consolidate a sort of green capitalism that aggravates and depoliticises the problem. However, the fight against climate change needs an innovative approach based on citizen politics and social transformation.