In the late 1980s, a new conceptual framework appeared in the science, technology, and innovation studies: the National Innovation System. The framework suggests that the research system's ultimate...

National Innovation System: The System Approach in Historical Perspective

At its inception, the sociology of science wag centrally concerned with relations between the scientific community and scientific knowledge on the one hand, and the wider society on the other hand. This was true, for instance of Merton's early work on the emergence of modern science' and on the norms governing scientific research.2 It was also true of Barber's attempt to provide a first systematic overview of the field.3 Recently, however, there has been a growing concentration upon the intemal characteristics of the research community, and particularly on the academic research community, with the wider social setting taken as given and relatively unproblematic.4 Those responsible for these more narrowly focused studies have not been unaware that the research community is subject to external influences. But they have assumed that, at least in some modem societies, it has sufficient autonomy to make a separate analysis of its internal processes acceptable as a first approximation. This research strategy has proved to be fruitful. It has enabled researchers to isolate a series of manageable problems and has produced results which have significantly improved our understanding of scientific communication,5 the reward system of science,6 social control in science,7 the development of research areas,8 and so on. However, this approach has had the unfortunate consequence of drawing attention away from the possibility of extending the analysis of the research community in such a way that connections with the

The Mediating Role of the Scientific Elite

Assessing publications through a bibliometric indicator: The case of comprehensive evaluation of scientific units in Poland

In this dissertation research, I try to deepen the understanding of the logic and history behind the science of science policy approaches and to substitute for this scientific evidence-based science policy model an evidence-critical and -informed model in which scientific and democratic claims are promoted simultaneously. The science of science policy, or what I call the scientizing science policy (SSP) discourse, is a strategic response of science policy community members to the following two socio-political developments: the government performance management reform movement and a new social contract of science. These two developments have motivated the science policy community to construct new science R&D management strategies that make science R&D investment more effective and economically beneficial than before. Former Presidential Science Advisor John Marburger played an important role in articulating an SSP approach at the federal level that opened up a political space for the larger SSP discourse to emerge and take hold. Other heterogeneous science policy community actors, including science agency managers and academic researchers, have also engaged and played major roles in shaping the premises, strategies, and directions that make up the SSP discourse by articulating their own approaches to SSP. The SSP discourse constitutes a series of strategies such as economizing and quantifying R&D investment decisions. In particular, to implement the ideas of performance reform and a new social contract of science in the field of science policy and management, the SSP community members have prioritized the development of data, models, and evidence related to federal R&D investment by funding studies on new scientific data, tools, and quantitative methods through the National Science Foundation (NSF) Science of Science and Innovation Policy (SciSIP) program. Interagency iii collaboration organized and supported by the Office of Science and Technology Policy (OSTP) is another key feature promoted by the SSP community. Through this research of the rise and development of the SSP discourse, I emphasize the following aspects that are relevant to both science policy practice and research community members. First, the SSP discourse demonstrates the influence of the performance reform movement on science, technology, and innovation policy and R&D management. Second, the SSP discourse has the strong potential to shift science policy makers’ focus from planning and implementing to evaluating federal R&D programs. Third, the SSP discourse not only reflects, but also promotes the tendency of public policy makers, politicians, and the public to rely on scientific claims and evidence when they are engaged in discussions or policy decision making processes related to science and technology. Fourth, the SSP discourse alters the balance of authority and influence among science policy actors, including science agency managers, scientists, and executive branch offices in the decision making process on federal R&D priority and investment. Fifth, even though there are conflicts and disagreements among science policy community members on the visions and future of the NSF SciSIP program, the SSP discourse is valuable as a space in which heterogeneous science policy research and practice community members can interact, learn from each other, and collaborate to develop U.S. science, technology, and innovation policy. I conclude by proposing an evidence-critical and -informed science policy in which the SSP discourse contributes to promoting democratic values in the science policy decision process. In particular, the evidence-critical and -informed model focuses on not only using scientific data and evidence when making federal R&D decisions, but also on promoting the democratic and deliberative process in monitoring R&D activities’ performance and social outcomes. In this model, I view the public as a legitimate stakeholder for evaluating federal R&D investment. This evidence-informed model can be implemented under the SSP discourse if the new R&D data, models, and tools developed by the NSF SciSIP-funded research are coupled with a new government performance website in which the public can access information about federal R&D activities as well as provide feedback about R&D investments to science policy makers. iv ACKNOWLEDGEMENTS I want to express my deepest appreciation to my committee chair, Dr. Saul Halfon, and my three committee members, including Dr. Daniel Breslau, Dr. Stephen Nelson, and Dr. Aaron Schroeder, for their academic advice in producing my dissertation. I thank Harvard Kennedy School STS fellow program director Dr. Sheila Jasanoff for offering me such a wonderful academic journey in her program to deepen my understanding of STS. I also appreciate for the NSF STS Program for providing a research grant to me for improving my dissertation research. And I would like to special thank to my wife, Eun-Hee Kim, my son, Isaac JaeHyun Kim, and my parents for their great support of my study.

Scientizing Science Policy: Implications for Science, Technology, and Innovation Policy and R&D Evaluation

RobeRt K. MeRton occupies a very special place in the study of science and techno­ logy, and I am humbled to be the first recipient of the award established in his honour by the International Sociological Association. When thinking about how to express my gratitude, I went back half a century ago, when I started working in the field of science, technology and development. A coincidence, clearly of the type Merton was fond of highlighting in his remarks about serendipity (Merton & Barber, 2004) led me to be in Lima at the time the National Research Council of Peru was created, and I was looking for a PhD dissertation topic in operations research and social systems sciences. Soon, I focused on how to design policy interventions and plan the development of science and technology capabilities in developing countries. This continues to be the most important issue, one of the most pressing in our times. While income and wealth inequalities between rich and poor countries have captured international attention, inequalities in science, technology and innova­ tion capabilities are much more pronounced. The average income per capita of the Organisation for Economic Co-operation and Development (OECD) countries— the rich countries club—is about sixty times greater than that of the low-income countries as defined by World Bank indicators; however, the number of scientific articles published per 100,000 inhabitants in rich countries is 170 times greater than that of low-income countries, and the number of patents registered in the former is 1,000 times greater than that in the latter (Sagasti, 2018). Bear in mind that rich countries have accumulated these advantages for a long time, and that the ‘Matthew effect’, clearly identified and reported by Merton (1968) and Zuckerman (1977),

T C Sinclair

Papers

Decision Making in National Science Policy