M ost studies of political action examine relatively "settled" times: actors may be in heated conflict, yet the rules of how this conflict will proceed are, by and large, not open to question. Even powerful elites find their actions channeled by the structure of government organizations (e.g., the nature of a representative legislature) and by existing political divisions (e.g., political parties). In some cases, however, the political structure that will guide future political action is created or altered through nothing other than political action itself. Rather than observing settled action, we then have the opportunity to observe settling action. In the most extreme form, we may find political actors crafting a new government structure-and thereby forming a new state. In this article, we examine one of the most consequential moments of such concerted state formation, the Constitutional Convention of 1787 that decided upon the basic political structure of the United States of America, and attempt to explain patterns of voting by state delegations.Perhaps because the national-level government of the United States was comparatively weak until the turn of the twentieth century (Skowronek 1982; also see Bensel 1990), sociologists have paid little attention to the Using data on state voting patterns, we examine the positions taken by state delegations on questions that arose over the course of the United States Constitutional Convention of 1787. Whereas existing accounts tend to assume that this type of collective decision making can be understood by linking fixed interests-either material or ideological-to specific, decontextualized propositions, we argue that the meaning of any one issue was dependent upon its position relative to other issues in the overall sequence of questions. Consequently, each decision changed the meaning of future issues, and hence how actors understood where their commonalities of interest lay. Devoted to the task of rebuilding the institutions that constituted the national state, delegates explicitly reshaped the board on which the political game would be played such that patterns of action within the Convention had implications for patterns of action outside of the Convention. As each subsequent decision within the Convention fixed a previous point of contention, it also indirectly determined which issues would become viable points of conflict in the future. By the end of the Convention, even before the first presidential election, state delegations began to arrange themselves in a manner consonant with the outlines of the first party system. This previously unrecognized finding only makes sense, however, in terms of a temporally contextualized model of political action.
Young and Holsteen (YH) introduce a number of tools for evaluating model uncertainty. In so doing, they are careful to differentiate their method from existing forms of model averaging. The fundamental difference lies in the way in which the underlying estimates are weighted. Whereas standard approaches to model averaging assign higher weight to better fitting models, the YH method weights all models equally. As I show, this is a nontrivial distinction, in that the two sets of procedures tend to produce radically different results. Drawing on both simulation and real-world examples, I demonstrate that in failing to distinguish between numerical variation and statistical uncertainty, the procedure proposed by YH will tend to overstate the amount of uncertainty resulting from variation across models. In standard circumstances, the quality of estimates produced using this method will tend to be objectively worse than that of conventional alternatives.
The authors present some relatively simple measures for the degree of organization of a social field, illustrating them with the intuitively accessible cases of fields that are organized by geographic space, and demonstrate that the measures are able to indicate when fields become more organized and that they can be applied to nongeographic data. Finally, the general approach highlights the danger of ignoring complexities of the functional form of spatial interdependence for social data.
Areal data have been used to good effect in a wide range of sociological research. One of the most persistent problems associated with this type of data, however, is the need to combine data sets with incongruous boundaries. To help address this problem, we introduce a new method for identifying common geographies. We show that identifying common geographies is equivalent to identifying components within a k-uniform k-partite hypergraph. This approach can be easily implemented using a geographic information system in conjunction with a simple search algorithm.
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