Article by Lonni Besançon, Corentin Segalas and Clémence Leyrat: “Although many concepts fall under the umbrella of Open Science, some of its key concepts are: Open Access, Open Data, Open Source, and Open Peer Review. How far these four principles were embraced by researchers during the pandemic and where there is room for improvement, is what we, as early career researchers, set out to assess by looking at data on scientific articles published during the Covid-19 pandemic….Open Source and Open Data practices consist in making all the data and materials used to gather or analyse data available on relevant repositories. While we can find incredibly useful datasets shared publicly on COVID-19 (for instance those provided by the European Centre for Disease Control), they remain the exception rather than the norm. A spectacular example of this were the papers utilising data from the company Surgisphere, that led to retracted papers in The Lancet and The New England Journal of Medicine. In our paper, we highlight 4 papers that could have been retracted much earlier (and perhaps would never have been accepted) had the data been made accessible from the time of publication. As we argue in our paper, this presents a clear case for making open data and open source the default, with exceptions for privacy and safety. While some journals already have such policies, we go further in asking that, when data cannot be shared publicly, editors/publishers and authors/institutions should agree on a third party to check the existence and reliability/validity of the data and the results presented. This not only would strengthen the review process, but also enhance the reproducibility of research and further accelerate the production of new knowledge through data and code sharing…(More)”.
Open Science: the Very Idea
Book by Frank Miedema: “This open access book provides a broad context for the understanding of current problems of science and of the different movements aiming to improve the societal impact of science and research.
The author offers insights with regard to ideas, old and new, about science, and their historical origins in philosophy and sociology of science, which is of interest to a broad readership. The book shows that scientifically grounded knowledge is required and helpful in understanding intellectual and political positions in various discussions on the grand challenges of our time and how science makes impact on society. The book reveals why interventions that look good or even obvious, are often met with resistance and are hard to realize in practice.
Based on a thorough analysis, as well as personal experiences in aids research, university administration and as a science observer, the author provides – while being totally open regarding science’s limitations- a realistic narrative about how research is conducted, and how reliable ‘objective’ knowledge is produced. His idea of science, which draws heavily on American pragmatism, fits in with the global Open Science movement. It is argued that Open Science is a truly and historically unique movement in that it translates the analysis of the problems of science into major institutional actions of system change in order to improve academic culture and the impact of science, engaging all actors in the field of science and academia…(More)”.
The Future is not a Solution
Essay by Laura Forlano: “The future is a particular kind of speaker,” explains communication scholar James W. Carey, “who tells us where we are going before we know it ourselves.” But in discussions about the nature of the future, the future as an experience never appears. This is because “the future is always offstage and never quite makes its entrance into history; the future is a time that never arrives but is always awaited.” Perhaps this is why, in the American context, there is a widespread tendency to “discount the present for the future,” and see the “future as a solvent” for existing social problems.
Abstract discussions of the “the future” miss the mark. That is because experience changes us. Anyone that has lived through the last 18 months of the COVID-19 pandemic would surely agree. While health experts are well aware of the ongoing global risks posed by pandemics, no one—not even an algorithm—can predict exactly when, where, and how they might come to be. And, yet, since spring 2020, there has been a global desire to understand precisely what is next, how to navigate uncertain futures as well as adapt to long-term changes. The pandemic, according to the writer Arundhati Roy, is “a portal, a gateway between one world and the next.”
In order to understand the choices that we are facing, it is necessary to understand the ways in which technologies and futures are often linked—socially, politically, and commercially —through their promises of a better tomorrow, one just beyond our grasp. Computer scientist Paul Dourish and anthropologist Genevieve Bell refer to these as “technovisions” or the stories that technologists and technology companies tell about the role of computational technologies in the future. Technovisions portray technological progress as inevitable—becoming cultural mythologies and self-fulfilling prophecies. They explain that the “proximate future,” a future that is “indefinitely postponed” is a key feature of research and practice in the field of computing that allows technology companies to “absolve themselves of the responsibilities of the present” by assuming that “certain problems will simply disappear of their own accord—questions of usability, regulation, resistance, adoption barriers, sociotechnical backlashes, and other concerns are erased.”…(More)”
For a heterodox computational social science
Paper by Petter Törnberg and Justus Uitermark: “The proliferation of digital data has been the impetus for the emergence of a new discipline for the study of social life: ‘computational social science’. Much research in this field is founded on the premise that society is a complex system with emergent structures that can be modeled or reconstructed through digital data. This paper suggests that computational social science serves practical and legitimizing functions for digital capitalism in much the same way that neoclassical economics does for neoliberalism. In recognition of this homology, this paper develops a critique of the complexity perspective of computational social science and argues for a heterodox computational social science founded on the meta-theory of critical realism that is critical, methodological pluralist, interpretative and explanative. This implies diverting computational social science’ computational methods and digital data so as to not be aimed at identifying invariant laws of social life, or optimizing state and corporate practices, but to instead be used as part of broader research strategies to identify contingent patterns, develop conjunctural explanations, and propose qualitatively different ways of organizing social life….(More)”.
What Universities Owe Democracy
Book by Ronald J. Daniels with Grant Shreve and Phillip Spector: “Universities play an indispensable role within modern democracies. But this role is often overlooked or too narrowly conceived, even by universities themselves. In What Universities Owe Democracy, Ronald J. Daniels, the president of Johns Hopkins University, argues that—at a moment when liberal democracy is endangered and more countries are heading toward autocracy than at any time in generations—it is critical for today’s colleges and universities to reestablish their place in democracy.
Drawing upon fields as varied as political science, economics, history, and sociology, Daniels identifies four distinct functions of American higher education that are key to liberal democracy: social mobility, citizenship education, the stewardship of facts, and the cultivation of pluralistic, diverse communities. By examining these roles over time, Daniels explains where colleges and universities have faltered in their execution of these functions—and what they can do going forward.
Looking back on his decades of experience leading universities, Daniels offers bold prescriptions for how universities can act now to strengthen democracy. For those committed to democracy’s future prospects, this book is a vital resource…(More)”.
Slowed canonical progress in large fields of science
Paper by Johan S. G. Chu and James A. Evans: “The size of scientific fields may impede the rise of new ideas. Examining 1.8 billion citations among 90 million papers across 241 subjects, we find a deluge of papers does not lead to turnover of central ideas in a field, but rather to ossification of canon. Scholars in fields where many papers are published annually face difficulty getting published, read, and cited unless their work references already widely cited articles. New papers containing potentially important contributions cannot garner field-wide attention through gradual processes of diffusion. These findings suggest fundamental progress may be stymied if quantitative growth of scientific endeavors—in number of scientists, institutes, and papers—is not balanced by structures fostering disruptive scholarship and focusing attention on novel ideas…(More)”.
Addressing bias in big data and AI for health care: A call for open science
Paper by Natalia Norori et al: “Bias in the medical field can be dissected along with three directions: data-driven, algorithmic, and human. Bias in AI algorithms for health care can have catastrophic consequences by propagating deeply rooted societal biases. This can result in misdiagnosing certain patient groups, like gender and ethnic minorities, that have a history of being underrepresented in existing datasets, further amplifying inequalities.
Open science practices can assist in moving toward fairness in AI for health care. These include (1) participant-centered development of AI algorithms and participatory science; (2) responsible data sharing and inclusive data standards to support interoperability; and (3) code sharing, including sharing of AI algorithms that can synthesize underrepresented data to address bias. Future research needs to focus on developing standards for AI in health care that enable transparency and data sharing, while at the same time preserving patients’ privacy….(More)”.
Citizen Science Project Builder 2.0
About by Citizen Science Center Zurich: “The Citizen Science Project Builder allows the implementation of Citizen Science projects, specifically in the area of data analysis. In such projects volunteers (“citizens”) collaborate with researchers in different kinds of scientific endeavors, from labeling images of snakes to transcribing handwritten Swiss German dialect, or classifying insects and plants. The Project Builder facilitates the implementation of such projects, supporting the collaborative analysis of large sets of digital data, including images and texts (i.e. satellite pictures, social media posts, etc.), as well as videos, audios, and scanned documents.
What makes the tool so special?
The Citizen Science Project Builder features a web interface that requires limited technical knowledge, and ideally little or no coding skills. It is a simple modular “step-by-step” system where a project can be created in just a few clicks. Once the project is set up, many people can easily be involved and start contributing to the analysis of data as well as providing feedback that will help you to improve your project!
What is new?
The new release of the Citizen Science Project Builder allows the building of full-fledged questionnaires for media analysis (including conditions and multiple formats for questions). A brand new functionality allows the geolocation of content on Open Street Map (e.g. mark the location of the content of an image) and also the delimitation of an area of interest (e.g. delimitate green areas). The interface still includes an “expert path” for developers, so if you can code (vue.js) the sky is the limit!…(More)”
The Case For Exploratory Social Sciences
Discussion Paper by Geoff Mulgan: “…Here I make the case for a new way of organising social science both in universities and beyond through creating sub-disciplines of ‘exploratory social science’ that would help to fill this gap. In the paper I show:
• how in the 18th and 19th centuries social sciences attempted to fuse interpretation and change
• how a series of trends – including quantification and abstraction – delivered advances but also squeezed out this capacity for radical design
• how these also encouraged some blind alleys for social science, including what I call ‘unrealistic realism’ and the futile search for eternal laws
I show some of the more useful counter-trends, including evolutionary thinking, systems models and complexity that create a more valid space for conscious design. I argue that now, at a time when we badly need better designs and strategies for the future, we face a paradoxical situation where the people with the deepest knowledge of fields are discouraged from systematic and creative exploration of the future, while those with the appetite and freedom to explore often lack the necessary knowledge…(More)”.
Algorithms Are Not Enough: Creating General Artificial Intelligence
Book by Herbert L. Roitblat: “Since the inception of artificial intelligence, we have been warned about the imminent arrival of computational systems that can replicate human thought processes. Before we know it, computers will become so intelligent that humans will be lucky to be kept as pets. And yet, although artificial intelligence has become increasingly sophisticated—with such achievements as driverless cars and humanless chess-playing—computer science has not yet created general artificial intelligence. In Algorithms Are Not Enough, Herbert Roitblat explains how artificial general intelligence may be possible and why a robopocalypse is neither imminent nor likely.
Existing artificial intelligence, Roitblat shows, has been limited to solving path problems, in which the entire problem consists of navigating a path of choices—finding specific solutions to well-structured problems. Human problem-solving, on the other hand, includes problems that consist of ill-structured situations, including the design of problem-solving paths themselves. These are insight problems, and insight is an essential part of intelligence that has not been addressed by computer science. Roitblat draws on cognitive science, including psychology, philosophy, and history, to identify the essential features of intelligence needed to achieve general artificial intelligence.
Roitblat describes current computational approaches to intelligence, including the Turing Test, machine learning, and neural networks. He identifies building blocks of natural intelligence, including perception, analogy, ambiguity, common sense, and creativity. General intelligence can create new representations to solve new problems, but current computational intelligence cannot. The human brain, like the computer, uses algorithms; but general intelligence, he argues, is more than algorithmic processes…(More)”.