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Literature, Science, and Public Policy shows how literature can influence scientific controversies and shape policy concerning evolution, genetics, and genomics.
This is part of a ten volume set of reference books offering authoritative and engaging critical overviews of the state of political science. This work explores the business end of politics, where theory meets practice in the pursuit of public good.
Basic scientific research and technological development have had an enormous impact on innovation, economic growth, and social well-being. Yet science policy debates have long been dominated by advocates for particular scientific fields or missions. In the absence of a deeper understanding of the changing framework in which innovation occurs, policymakers cannot predict how best to make and manage investments to exploit our most promising and important opportunities. Since 2005, a science of science policy has developed rapidly in response to policymakers' increased demands for better tools and the social sciences' capacity to provide them. The Science of Science Policy: A Handbook brings together some of the best and brightest minds working in science policy to explore the foundations of an evidence-based platform for the field. The contributions in this book provide an overview of the current state of the science of science policy from three angles: theoretical, empirical, and policy in practice. They offer perspectives from the broader social science, behavioral science, and policy communities on the fascinating challenges and prospects in this evolving arena. Drawing on domestic and international experiences, the text delivers insights about the critical questions that create a demand for a science of science policy.
The distinction between basic and applied research was central to twentieth-century science and policymaking, and if this framework has been contested in recent years, it nonetheless remains ubiquitous in both scientific and public discourse. Employing a transnational, diachronic perspective informed by historical semantics, this volume traces the conceptual history of the basic–applied distinction from the nineteenth century to today, taking stock of European developments alongside comparative case studies from the United States and China. It shows how an older dichotomy of pure and applied science was reconceived in response to rapid scientific progress and then further transformed by the geopolitical circumstances of the postwar era.
Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government.
"Studying Publisc Policy develops an analytical framework of the subject for students in public policy course. Instead of focussing on the substantive policy of a particular policy area, the book examines the theoretical and conceptual foundations of, and approaches used in, the policy sciences."--Provided by publisher.
The role of science in policymaking has gained unprecedented stature in the United States, raising questions about the place of science and scientific expertise in the democratic process. Some scientists have been given considerable epistemic authority in shaping policy on issues of great moral and cultural significance, and the politicizing of these issues has become highly contentious. Since World War II, most philosophers of science have purported the concept that science should be "value-free." In Science, Policy and the Value-Free Ideal, Heather E. Douglas argues that such an ideal is neither adequate nor desirable for science. She contends that the moral responsibilities of scientists require the consideration of values even at the heart of science. She lobbies for a new ideal in which values serve an essential function throughout scientific inquiry, but where the role values play is constrained at key points, thus protecting the integrity and objectivity of science. In this vein, Douglas outlines a system for the application of values to guide scientists through points of uncertainty fraught with moral valence.Following a philosophical analysis of the historical background of science advising and the value-free ideal, Douglas defines how values should-and should not-function in science. She discusses the distinctive direct and indirect roles for values in reasoning, and outlines seven senses of objectivity, showing how each can be employed to determine the reliability of scientific claims. Douglas then uses these philosophical insights to clarify the distinction between junk science and sound science to be used in policymaking. In conclusion, she calls for greater openness on the values utilized in policymaking, and more public participation in the policymaking process, by suggesting various models for effective use of both the public and experts in key risk assessments.
A calculation of the social returns to innovation /Benjamin F. Jones and Lawrence H. Summers --Innovation and human capital policy /John Van Reenen --Immigration policy levers for US innovation and start-ups /Sari Pekkala Kerr and William R. Kerr --Scientific grant funding /Pierre Azoulay and Danielle Li --Tax policy for innovation /Bronwyn H. Hall --Taxation and innovation: what do we know? /Ufuk Akcigit and Stefanie Stantcheva --Government incentives for entrepreneurship /Josh Lerner.
Field experiments -- randomized controlled trials -- have become ever more popular in political science, as well as in other disciplines, such as economics, social policy and development. Policy-makers have also increasingly used randomization to evaluate public policies, designing trials of tax reminders, welfare policies and international aid programs to name just a few of the interventions tested in this way. Field experiments have become successful because they assess causal claims in ways that other methods of evaluation find hard to emulate. Social scientists and evaluators have rediscovered how to design and analyze field experiments, but they have paid much less attention to the challenges of organizing and managing them. Field experiments pose unique challenges and opportunities for the researcher and evaluator which come from working in the field. The research experience can be challenging and at times hard to predict. This book aims to help researchers and evaluators plan and manage their field experiments so they can avoid common pitfalls. It is also intended to open up discussion about the context and backdrop to trials so that these practical aspects of field experiments are better understood. The book sets out ten steps researchers can use to plan their field experiments, then nine threats to watch out for when they implement them. There are cases studies of voting and political participation, elites, welfare and employment, nudging citizens, and developing countries.