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Consensus reports emerged in the mid-twentieth century as a means for scientists to give advice to governments. For the scientists involved, the goal of consensus reflects a belief in the power of univocality: that a single, consistent message would be more likely to be influential than alternatives, such as expressing majority and minority views. Scientists in the mid-twentieth century perhaps intuitively perceived what social science research has since demonstrated: that expert disagreement, or even the appearance of it, can undermine public confidence in experts and the science they are trying to communicate. However, the concept of consensus as displayed in the three cases studied here is not easily reducible to simple univocality; consensus appears to have taken multiple forms in various institutional contexts. There is no singular established definition of consensus at work within assessments, nor a universally accepted set of rules by which it should be achieved. Modern assessments are distinguished by their large scale and institutionalization; consensus is often a key element, but assessments also introduce scientists to policy concerns, help to guide research in policy-relevant directions, and provide scientists and policymakers with an area of overlapping concern.

Group judgments are often influenced by their members’ individual expertise. It is less clear, though, how individual expertise should affect the group judgments. This chapter surveys a wide range of models of opinion aggregation and group judgment: models where all group members have the same impact on the group judgment, models that take into account differences in individual accuracy, and models where group members revise their beliefs as a function of their mutual respect. The scope of these models covers the aggregation of propositional attitudes, probability functions, and numerical estimates. By comparing these different kinds of models and contrasting them with findings in psychology, management science, and the expert judgment literature, the chapter provides a better understanding of the role of expertise in group agency, both from a theoretical and from an empirical perspective.

The chapter provides a summary of the previous chapters’ conclusions, a survey of the whole book. Measurement is the assignment of numbers to objects or events according to a rule. The rule should be such that the numbers provide reliable information about the objects or events. But the rules applicable in the field are different from the rules used in the laboratory. Measurement practices in different social field sciences show that for measurement expert judgment is indispensable. But expert judgments tend to disagree and are not equally good. To have measurement outside the laboratory as objective as possible, one needs to find a consensus of the multiple expert judgments in a way that accounts for the performance of the individual experts. But to account for the quality of these performances in social science, instead of individual scientists, one has to evaluate the expertise of institutions.

The central puzzle of this chapter is the meteoric rise and abrupt fall in the popularity of Ferenc Gyurcsány, the Hungarian prime minister between 2004 and 2009. The chapter applies the LCI to explain this riddle by analyzing his prime-ministerial career. The chapter also aims to contribute to the methodological refinement of the LCI. First, it introduces a milestone approach, which sets the data for six crucial moments in Gyurcsány’s political career to make the LCI a dynamic tool for the analysis. Second, in order to improve the reliability of the method and exclude researcher bias, it replaces researcher judgment with expert judgment in the cases of communicative performance and management skills, and with the fulfillment rate of the legislative program in the case of parliamentary effectiveness. The result of the research diverges from our initial expectations, since the aggregate value of the LCI decreased only rather moderately.