Research collaborations beyond the boundaries of science—such as transdisciplinary, participatory or co-research projects—usually aim at increasing the societal impact of the research conducted. In the literature discussing such collaborations, as well as in science policy endorsing them, it is generally assumed that the wanted societal impact is achieved through exchange that contributes to knowledge production and to the results of the research. However, collaboration beyond the boundaries of science can help a research project reach its societal impact goals even if (...) it does not contribute to the epistemic outcomes of the project at all. Instead, other kinds of contributions from the extra-academic partners, and what the extra-academic partners receive from the collaboration, can be crucial. Recognizing this helps us to better understand existing practices, and to identify potentially interesting forms of collaboration beyond the boundaries of science. (shrink)

Data science is not simply a method but an organising idea. Commitment to the new paradigm overrides concerns caused by collateral damage, and only a counterculture can constitute an effective critique. Understanding data science requires an appreciation of what algorithms actually do; in particular, how machine learning learns. The resulting ‘insight through opacity’ drives the observable problems of algorithmic discrimination and the evasion of due process. But attempts to stem the tide have not grasped the nature of data science as (...) both metaphysical and machinic. Data science strongly echoes the neoplatonism that informed the early science of Copernicus and Galileo. It appears to reveal a hidden mathematical order in the world that is superior to our direct experience. The new symmetry of these orderings is more compelling than the actual results. Data science does not only make possible a new way of knowing but acts directly on it; by converting predictions to pre-emptions, it becomes a machinic metaphysics. The people enrolled in this apparatus risk an abstraction of accountability and the production of ‘thoughtlessness’. Susceptibility to data science can be contested through critiques of science, especially standpoint theory, which opposes the ‘view from nowhere’ without abandoning the empirical methods. But a counterculture of data science must be material as well as discursive. Karen Barad’s idea of agential realism can reconfigure data science to produce both non-dualistic philosophy and participatory agency. An example of relevant praxis points to the real possibility of ‘machine learning for the people’. (shrink)

Bijker calls for scholars in science and technology studies to become public intellectuals by actively working toward “democratizing... technological culture.” Many STS scholars have developed practices that support democratic and public activity; yet, these typically require individual commitment with inadequate institutional support. The public work of STS scholars can be better supported through a program that includes using specialist research in nonreproductive educational contexts, redefining and revaluing academic service, developing more accessible ways of writing, and publishing and valuing STS-based texts (...) that circulate among nonacademic audiences. (shrink)

Quantitative information is one of the means used to interface science with policy. As a consequence, much effort is invested in producing quantitative information for policy and much criticism is directed toward the use of numbers in policy. In this paper, I analyze five approaches drawn from such criticisms and propose alternative uses of quantitative information for governance: valuation of ecosystem services, social multicriteria evaluation, quantification of uncertainty through the Numeral, Unit, Spread, Assessment, Pedigree approach, Quantitative Story-Telling, and the heuristic (...) use of statistics. The analysis shows the varied ways that numbers are conceptualized and how different conceptualizations matter for the science–policy interface. Alternative conceptualizations of numbers are used to challenge the model of science-speaking truth to power. Uncertainty, complexity, pluralism, malpractice, and values are mobilized to redefine the relations between science and policy. Alternative quantification may produce alternative facts, but reflexive approaches that use numbers to discuss the relevance of equity, positionality, and quality in science for policy may offer a remedy. (shrink)

This article highlights that in our rush to call for the democratization of science and expertise we must not forget to speak to what type of democratization we are calling for. In short, not all participatory forms are the same. In developing this argument, I examine one such form that has yet to receive much attention from science and technology studies scholars: the agricultural field day. In examining the field day, we find that its orientation—that is, toward either the conventional (...) or sustainable model of agriculture—shapes significantly its democratic approach to knowledge coproduction. To help inform how we understand conventional and sustainable field days, I turned to two conceptual frameworks of participation: namely, that of Wynne and Funtowicz and Ravetz. In doing this, we find that conventional field days mirror closely that form of public participation detailed by Funtowicz and Ravetz, while sustainable field days reflect that conceptualized by Wynne. (shrink)