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L 87 E ven when making empirical claims, scientists have the same moral responsibilities as the general population to consider the consequences of error. This apparently unremarkable statement has some remarkable implications. It means that scientists should consider the potential social and ethical consequences of error in their work, that they should weigh the importance of those consequences, and that they should set burdens of proof accordingly. Social and ethical values are needed to make these judgments, not just as a matter of an accurate description of scientific practice, but as part of an ideal for scientific reasoning. Thus, the value-free ideal for science is a bad ideal. However, simply discarding the ideal is insufficient. Although scientists need to consider values when doing science, there must be constraints on how values are considered, on what role they play in the reasoning process. For example, simply because a scientist values (or would prefer) a particular outcome of a study does not mean the scientist’s preference should be taken as a reason in itself to accept the outcome. Values are not evidence; wishing does not make it so. There must be some important limits to the roles values play in science. To find these limits, it is time to explore and map the territory of values in science. This will allow me to articulate a new ideal for values in science, a revised understanding of how values should play a role in science and of what the structure of values in science should be. I will argue that in general there are two roles for values in scientific reasoning: a direct role and Chapter 5 The Structure of Values in Science Douglas text.indd 87 4/16/09 2:47:10 PM 88 • the structure of values in science an indirect role. The distinction between these two roles is crucial. While values can play an indirect role throughout the scientific process, values should play a direct role only for certain kinds of decisions in science. This distinction between direct and indirect roles allows for a better understanding of the place of values in science—values of any kind, whether cognitive , ethical, or social. The crucial normative boundary is to be found not among the kinds of values scientists should or should not consider (as the traditional value-free ideal holds), but among the particular roles for values in the reasoning process. The new ideal that rests on this distinction in roles holds for all kinds of scientific reasoning, not just science in the policy process, although the practical import of the ideal may be most pronounced for policy-relevant science. In order to map the values in science terrain, we need to consider the function of values throughout the scientific research process. The schema of the research process I use in this chapter is admittedly idealized, but it should be both familiar and a sufficient approximation. The first step in any research endeavor is deciding which questions to pursue, which research problems to tackle. This decision ranges from the rather vague (“I think I’ll look here”) to the very precise (a particular approach to a very well-defined problem). Regardless, a decision, a judgment, must be made to get the process started. Then the researcher must select a particular methodology in order to tackle the problem. This decision is often closely tied to the choice to pursue a particular problem, but in many cases it is not. These decisions are often under constraints of ethical acceptability, resource limitations, and skill sets. And if institutional review boards are overseeing methodological choices, these decisions can take place over a protracted period of time. Methodological choices also profoundly shape where one looks for evidence. Once the researcher embarks upon a chosen methodology, he or she must decide how to interpret events in the study in order to record them as data. In many cases, this is a straightforward decision with little need for judgment. However, judgment may be called for on whether a particular event occurred within the framework of the methodological protocol (was the equipment working properly?), or judgment may be needed in how to best characterize an ambiguous event. Once the researcher has collected the data, he or she must interpret it and draw conclusions. The scientist must ultimately decide whether the data support the hypothesis, and whether to accept or reject a theory based on the evidence. This process is mimicked in the papers published by scientists, and thus...


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