Essay by Anastasia Buyalskaya, Marcos Gallo and Colin Camerer: “In this short essay we argue that social science is entering a golden age, marked by explosive growth in new data and analytic methods, interdisciplinarity, and a recognition that both of those ingredients are necessary to solve hard problems. Two examples are given to illustrate these themes, which are behavioral economics and social networks. Numerous other specific study examples are then given. We also address the challenges that accompany the three positive trends, which include informatics, career incentives, and the search for unifying frameworks….(More)”.
Essay by Steven Shapin: “…It seems irresponsible or perverse to reject the idea that there is a Crisis of Truth. No time now for judicious reflection; what’s needed is a full-frontal attack on the Truth Deniers. But it’s good to be sure about the identity of the problem before setting out to solve it. Conceiving the problem as a Crisis of Truth, or even as a Crisis of Scientific Authority, is not, I think, the best starting point. There’s no reason for complacency, but there is reason to reassess which bits of our culture are in a critical state and, once they are securely identified, what therapies are in order.
Start with the idea of Truth. What could be more important, especially if the word is used — as it often is in academic writing — as a placeholder for Reality? But there’s a sort of luminous glow around the notion of Truth that prejudges and pre-processes the attitudes proper to entertain about it. The Truth goes marching on. God is Truth. The Truth shall set you free. Who, except the mad and the malevolent, could possibly be against Truth? It was, after all, Pontius Pilate who asked, “What is Truth?” — and then went off to wash his hands.
So here’s an only apparently pedantic hint about how to construe Truth and also about why our current problem might not be described as a Crisis of Truth. In modern common usage, Truth is a notably uncommon term. The natural home of Truth is not in the workaday vernacular but in weekend, even language-gone-on-holiday, scenes. The notion of Truth tends to crop up when statements about “what’s the case” are put under pressure, questioned, or picked out for celebration. Statements about “the case” can then become instances of the Truth, surrounded by an epistemic halo. Truth is invoked when we swear to tell it — “the whole Truth and nothing but” — in legal settings or in the filling-out of official forms when we’re cautioned against departing from it; or in those sorts of school and bureaucratic exams where we’re made to choose between True and False. Truth is brought into play when it’s suspected that something of importance has been willfully obscured — as when Al Gore famously responded to disbelief in climate change by insisting on “an inconvenient truth” or when we demand to be told the Truth about the safety of GMOs. 
Truth-talk appears in such special-purpose forums as valedictory statements where scientists say that their calling is a Search for Truth. And it’s worth considering the difference between saying that and saying they’re working to sequence a breast cancer gene or to predict when a specific Indonesian volcano is most likely to erupt. Truth stands to Matters-That-Are-the-Case roughly as incantations, proverbs, and aphorisms stand to ordinary speech. Truth attaches more to some formal intellectual practices than to others — to philosophy, religion, art, and, of course, science, even though in science there is apparent specificity. Compare those sciences that seem good fits with the notion of a Search for Truth to those that seem less good fits: theoretical physics versus seismology, academic brain science versus research on the best flavoring for a soft drink. And, of course, Truth echoes around philosophy classrooms and journals, where theories of what it is are advanced, defended, and endlessly disputed. Philosophers collectively know that Truth is very important, but they don’t collectively know what it is.
I’ve said that Truth figures in worries about the problems of knowledge we’re said to be afflicted with, where saying that we have a Crisis of Truth both intensifies the problem and gives it a moral charge. In May 2019, Angela Merkel gave the commencement speech at Harvard. Prettily noting the significance of Harvard’s motto, Veritas, the German Chancellor described the conditions for academic inquiry, which, she said, requires that “we do not describe lies as truth and truth as lies,” nor that “we accept abuses [Missstände] as normal.” The Harvard audience stood and cheered: they understood the coded political reference to Trump and evidently agreed that the opposite of Truth was a lie — not just a statement that didn’t match reality but an intentional deception. You can, however, think of Truth’s opposite as nonsense, error, or bullshit, but calling it a lie was to position Truth in a moral field. Merkel was not giving Harvard a lesson in philosophy but a lesson in global civic virtue….(More)”.
Book by Chris Lintott: “The world of science has been transformed. Where once astronomers sat at the controls of giant telescopes in remote locations, praying for clear skies, now they have no need to budge from their desks, as data arrives in their inbox. And what they receive is overwhelming; projects now being built provide more data in a few nights than in the whole of humanity’s history of observing the Universe. It’s not just astronomy either – dealing with this deluge of data is the major challenge for scientists at CERN, and for biologists who use automated cameras to spy on animals in their natural habitats. Artificial intelligence is one part of the solution – but will it spell the end of human involvement in scientific discovery?
No, argues Chris Lintott. We humans still have unique capabilities to bring to bear – our curiosity, our capacity for wonder, and, most importantly, our capacity for surprise. It seems that humans and computers working together do better than computers can on their own. But with so much scientific data, you need a lot of scientists – a crowd, in fact. Lintott found such a crowd in the Zooniverse, the web-based project that allows hundreds of thousands of enthusiastic volunteers to contribute to science.
In this book, Lintott describes the exciting discoveries that people all over the world have made, from galaxies to pulsars, exoplanets to moons, and from penguin behavior to old ship’s logs. This approach builds on a long history of so-called “citizen science,” given new power by fast internet and distributed data. Discovery is no longer the remit only of scientists in specialist labs or academics in ivory towers. It’s something we can all take part in. As Lintott shows, it’s a wonderful way to engage with science, yielding new insights daily. You, too, can help explore the Universe in your lunch hour…(More)”.
Book by Virginia Dignum: “In this book, the author examines the ethical implications of Artificial Intelligence systems as they integrate and replace traditional social structures in new sociocognitive-technological environments. She discusses issues related to the integrity of researchers, technologists, and manufacturers as they design, construct, use, and manage artificially intelligent systems; formalisms for reasoning about moral decisions as part of the behavior of artificial autonomous systems such as agents and robots; and design methodologies for social agents based on societal, moral, and legal values.
Throughout the book the author discusses related work, conscious of both classical, philosophical treatments of ethical issues and the implications in modern, algorithmic systems, and she combines regular references and footnotes with suggestions for further reading. This short overview is suitable for undergraduate students, in both technical and non-technical courses, and for interested and concerned researchers, practitioners, and citizens….(More)”.
Book by Miguel A. Hernán, James M. Robins: “Causal Inference is an admittedly pretentious title for a book. Causal inference is a complex scientific task that relies on triangulating evidence from multiple sources and on the application of a variety of methodological approaches. No book can possibly provide a comprehensive description of methodologies for causal inference across the sciences. The authors of any Causal Inference book will have to choose which aspects of causal inference methodology they want to emphasize.
The title of this introduction reflects our own choices: a book that helps scientists–especially health and social scientists–generate and analyze data to make causal inferences that are explicit about both the causal question and the assumptions underlying the data analysis. Unfortunately, the scientific literature is plagued by studies in which the causal question is not explicitly stated and the investigators’ unverifiable assumptions are not declared. This casual attitude towards causal inference has led to a great deal of confusion. For example, it is not uncommon to find studies in which the effect estimates are hard to interpret because the data analysis methods cannot appropriately answer the causal question (were it explicitly stated) under the investigators’ assumptions (were they declared).
In this book, we stress the need to take the causal question seriously enough to articulate it, and to delineate the separate roles of data and assumptions for causal inference. Once these foundations are in place, causal inferences become necessarily less casual, which helps prevent confusion. The book describes various data analysis approaches that can be used to estimate the causal effect of interest under a particular set of assumptions when data are collected on each individual in a population. A key message of the book is that causal inference cannot be reduced to a collection of recipes for data analysis.
The book is divided in three parts of increasing difficulty: Part I is about causal inference without models (i.e., nonparametric identification of causal effects), Part II is about causal inference with models (i.e., estimation of causal effects with parametric models), and Part III is about causal inference from complex longitudinal data (i.e., estimation of causal effects of time-varying treatments)….(More) (Additional Material)”.
Editorial by Howard Bauchner in JAMA: “The goal of making science more transparent—sharing data, posting results on trial registries, use of preprint servers, and open access publishing—may enhance scientific discovery and improve individual and population health, but it also comes with substantial challenges in an era of politicized science, enhanced skepticism, and the ubiquitous world of social media. The recent announcement by the Trump administration of plans to proceed with an updated version of the proposed rule “Strengthening Transparency in Regulatory Science,” stipulating that all underlying data from studies that underpin public health regulations from the US Environmental Protection Agency (EPA) must be made publicly available so that those data can be independently validated, epitomizes some of these challenges. According to EPA Administrator Andrew Wheeler: “Good science is science that can be replicated and independently validated, science that can hold up to scrutiny. That is why we’re moving forward to ensure that the science supporting agency decisions is transparent and available for evaluation by the public and stakeholders.”
Virtually every time JAMA publishes an article on the effects of pollution or climate change on health, the journal immediately receives demands from critics to retract the article for various reasons. Some individuals and groups simply do not believe that pollution or climate change affects human health. Research on climate change, and the effects of climate change on the health of the planet and human beings, if made available to anyone for reanalysis could be manipulated to find a different outcome than initially reported. In an age of skepticism about many issues, including science, with the ability to use social media to disseminate unfounded and at times potentially harmful ideas, it is challenging to balance the potential benefits of sharing data with the harms that could be done by reanalysis.
Can the experience of sharing data derived from randomized clinical trials (RCTs)—either as mandated by some funders and journals or as supported by individual investigators—serve as examples as a way to safeguard “truth” in science….
Although the sharing of data may have numerous benefits, it also comes with substantial challenges particularly in highly contentious and politicized areas, such as the effects of climate change and pollution on health, in which the public dialogue appears to be based on as much fiction as fact. The sharing of data, whether mandated by funders, including foundations and government, or volunteered by scientists who believe in the principle of data transparency, is a complicated issue in the evolving world of science, analysis, skepticism, and communication. Above all, the scientific process—including original research and reanalysis of shared data—must prevail, and the inherent search for evidence, facts, and truth must not be compromised by special interests, coercive influences, or politicized perspectives. There are no simple answers, just words of caution and concern….(More)”.
Book by Robert Kozinets: “Netnography is an adaptation of ethnography for the online world, pioneered by Robert Kozinets, and is concerned with the study of online cultures and communities as distinct social phenomena, rather than isolated content. In this landmark third edition, Netnography: The Essential Guide provides the theoretical and methodological groundwork as well as the practical applications, helping students both understand and do netnographic research projects of their own.
Packed with enhanced learning features throughout, linking concepts to structured activities in a step by step way, the book is also now accompanied by a striking new visual design and further case studies, offering the essential student resource to conducting online ethnographic research. Real world examples provided demonstrate netnography in practice across the social sciences, in media and cultural studies, anthropology, education, nursing, travel and tourism, and others….(More)”.
Paper by Irene V. Pasquetto, Christine L. Borgman, and Morgan F. Wofford: “Open access to data, as a core principle of open science, is predicated on assumptions that scientific data can be reused by other researchers. We test those assumptions by asking where scientists find reusable data, how they reuse those data, and how they interpret data they did not collect themselves. By conducting a qualitative meta-analysis of evidence on two long-term, distributed, interdisciplinary consortia, we found that scientists frequently sought data from public collections and from other researchers for comparative purposes such as “ground-truthing” and calibration. When they sought others’ data for reanalysis or for combining with their own data, which was relatively rare, most preferred to collaborate with the data creators.
We propose a typology of data reuses ranging from comparative to integrative. Comparative data reuse requires interactional expertise, which involves knowing enough about the data to assess their quality and value for a specific comparison such as calibrating an instrument in a lab experiment. Integrative reuse requires contributory expertise, which involves the ability to perform the action, such as reusing data in a new experiment. Data integration requires more specialized scientific knowledge and deeper levels of epistemic trust in the knowledge products. Metadata, ontologies, and other forms of curation benefit interpretation for any kind of data reuse. Based on these findings, we theorize the data creators’ advantage, that those who create data have intimate and tacit knowledge that can be used as barter to form collaborations for mutual advantage. Data reuse is a process that occurs within knowledge infrastructures that evolve over time, encompassing expertise, trust, communities, technologies, policies, resources, and institutions….(More)”.
Paper by Liz Dowthwaite et al: “Increasing motivation to contribute to online citizen science projects can improve user experience and is critical in retaining and attracting users. Drawing on previous studies of motivation, this paper suggests self-determination theory as a framework for explaining the psychological constructs behind participation in Citizen Science. Through examining existing studies of motivation for 6 Zooniverse projects through this lens, the paper suggests how appealing to basic psychological needs could increase participation in online citizen science, considering current practices and directions for future developments and research….(More)”.
Paper by Peter Bragge: “Applying knowledge to problems has occupied the minds of great philosophers, scientists and other thinkers for centuries. In more modern times, the challenge of connecting knowledge to practice has been addressed through fields such as evidence-based medicine which have conceptualised optimal healthcare as integration of best available research evidence, clinical experience and patients’ values. Similar principles apply to evidence-based public policy, and literature in this field has been growing since the turn of the century.
The exponential rise in knowledge availability has greatly enhanced the ‘supply’ side of the evidence-into-practice equation – however substantial gaps between evidence and practice remain. Policymakers are therefore increasingly looking to academia to optimise evidence-informed policy. This article presents ten considerations for optimising evidence-based policy, drawn from experience in delivering applied behaviour change research to government….(More)”.