Conflicting goals
The concern that led to the implementation of SciSIP was that the dominant model of science policy did not satisfactorily describe the relationship between science and society. Therefore, the goal of SciSIP should presumably be to analyze that relationship, questioning both the process and the outcomes of science and technology. But the stated goals of SciSIP do not match up with these larger concerns. In fact, the program can be seen as counter-productive to these goals, since it reinforces the system it was meant to critique. Fulfilling the goals of SciSIP, as they are stated and overwhelmingly pursued in current SciSIP-funded projects, will provide information about the relationship between investment in science and innovation and economic output, but such knowledge will do nothing to further our understanding about how science is actually used in society, and to what ends.
SciSIP’s purpose, to better understand the societal impacts of investment in R&D, can be thought of two different ways. The first is to measure the relationship between inputs and outputs: this is what SciSIP is largely doing. The second, which I argue is much more urgently needed than the first but remains largely unaddressed by SciSIP, is to question the real outcomes of science – both positive and negative – and to evaluate how science is used in society, and to what end.
There are three major problems with the assumptions of the SciSIP program as it is currently conceived. The first has to do with SciSIP’s emphasis on economic progress. It is true that the standard measure of progress is in terms of GDP. This is reinforced by the many SciSIP-funded projects that aim to track the relationship between science funding and national wealth. But even if the reservoir model is correct in citing a correlation between increased funding of science and increased national wealth, the assumption that increased wealth is equivalent to increased wellbeing is highly suspect. In fact, there is some evidence that after a certain level of basic needs is met there is little or no correlation between material wealth or goods and personal satisfaction.
Moreover, increases in wealth often flow disproportionately to the wealthiest in society. Even advances in biotechnology are questionable in terms of their contribution to basic societal benefit: new drugs are for-profit and often aid only a small percentage of the population. More science and technology are often accompanied by increases in inequality (Sarewitz et al 2004, 69). This inequality manifests itself in high unemployment and underemployment rates, persistent levels of poverty, and ever-increasing concentrations of wealth and poverty, despite greater overall national and global wealth. In the case of unemployment, employers often adopt new technologies with the explicit goal of increasing efficiency in order to reduce employment. Innovation policy that concentrates on increasing innovation without consideration of the social consequences of innovation will fall short of the goal of overall societal benefit.
Second, a more scientific and technological society brings with it new risks and hazards (Jasanoff 2003 p. 224). We no longer have the luxury of assuming that all science and technology is for the good of humanity. Bill Joy, co-founder and Chief Scientist of Sun Microsystems, describes with alarming clarity one possible scenario of scientific “progress:” a future in which machines render humans irrelevant. His point is not one of science fiction but of the very real possibility of unintended consequences – many disastrous to the future of humanity – that accompany scientific breakthroughs and the adoption of novel technologies. An alternative direction for SciSIP, one that addresses both of the previous critiques, would be to examine the range of societal changes -- positive, negative, and indeterminate -- that result from more science and technology, more innovation, and more wealth.
The third reason has to do with the use of science for better decision-making. The assumption here, much like the myth of infinite benefit, is that increased science funding equals better policy. One of the goals of SciSIP is to determine future science policy funding levels by measuring the rate of return on investment. The SciSIP Prospectus projects “that future policy decisions may be made based on current data, sound science and informed judgment, more closely analogous to the way that economic models have helped economic policy makers” (NSF 2006, 1). But data about returns on investment does not provide any information about the use or outcome of science research.
Related to this is the use of science for policy-making in general. By focusing primarily on measuring the inputs and outputs of the standard reservoir model, we are still not asking questions about what kind of knowledge is useful for policy-makers, and for what purpose. Science remains isolated from its societal implications, in the sense that the science is done independently, and information is provided to users (in this example, policy-makers). SciSIP does not call this arrangement into question. But one obvious problem with the business-as-usual formula, which SciSIP aims to measure but not to challenge, is that policymakers (users) are not involved in the funding process to influence the direction of research. More knowledge is not necessarily helpful for decision-making because it does not meet the needs of those making the decisions. In order to truly influence the success of investments in knowledge for the purpose of improving decision-making, it is necessary to ask questions such as: What knowledge do decision-makers truly need? What kinds of problems can be informed by scientific information, and what cannot? What kinds of problems have technical solutions, and what kinds of problems are political in nature?
