Social Theory, Social Research, and a Theory of Action

Coleman, James S.

doi:10.1086/228423

Cited by 1,191 publications

(616 citation statements)

References 32 publications

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“…This relational exchange is organized in policy networks (Laumann and Knoke 1987). Although it is widely acknowledged that final decision control is the most important resource in policy-making, authors largely agree that this control can be exchanged for influence resources such as information, public support, or technical expertise (Coleman 1986;Choi and Robertson 2013;Henning 2009;Knoke et al 1996;Leifeld and Schneider 2012;Henning 1999, 1998). The possession of these influence resources considerably facilitates direct access to decision making and policy design and increases actors' reputational power (Beyers and Braun 2013;Heaney 2014;Raub and Weesie 1990).…”

Section: Structural Powermentioning

confidence: 99%

Structural and Institutional Determinants of Influence Reputation: A Comparison of Collaborative and Adversarial Policy Networks in Decision Making and Implementation

Ingold

Leifeld

2014

JOPART

133

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The central assumption in the literature on collaborative networks and policy networks is that political outcomes are affected by a variety of state and nonstate actors. Some of these actors are more powerful than others and can therefore have a considerable effect on decision making. In this article, we seek to provide a structural and institutional explanation for these power differentials in policy networks and support the explanation with empirical evidence. We use a dyadic measure of influence reputation as a proxy for power, and posit that influence reputation over the political outcome is related to vertical integration into the political system by means of formal decisionmaking authority, and to horizontal integration by means of being well embedded into the policy network. Hence, we argue that actors are perceived as influential because of two complementary factors: (a) their institutional roles and (b) their structural positions in the policy network. Based on temporal and cross-sectional exponential random graph models, we compare five cases about climate, telecommunications, flood prevention, and toxic chemicals politics in Switzerland and Germany. The five networks cover national and local networks at different stages of the policy cycle. The results confirm that institutional and structural drivers seem to have a crucial impact on how an actor is perceived in decision making and implementation and, therefore, their ability to significantly shape outputs and service delivery.

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Section: Structural Powermentioning

confidence: 99%

Structural and Institutional Determinants of Influence Reputation: A Comparison of Collaborative and Adversarial Policy Networks in Decision Making and Implementation

Ingold

Leifeld

2014

JOPART

133

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“…This provides a stylized generalization of various analytic approaches to social science (e.g. , Coleman 1990;Hedströ m 2005;Elster 2007), depicted in figure 1. In this model, immediate causes of action at any point in time are embodied characteristics of the person ( ) and characteristics of actor's P i circumstances ( ) that together define the immediate available oppor-E i tunities for action.…”

Section: Genetics and Social Causesmentioning

confidence: 99%

“…9 With many notable exceptions, social surveys have historically been relatively stinting in their measurement of basic psychological characteristics, focusing instead on measures of past experiences and present circumstances, as well as more domain-specific psychological characteristics (e.g., attitudes on specific political issues). This has only increased the difficulties for social science to use survey resources to produce better substantiated inferences about how past experiences affect the present and may contribute to the lack of specific action-level thinking that has repeatedly frustrated some observers of sociological practice (Coleman 1986;Hedströ m 2005). In any case, although genetics and biological information may help in the measurement of psychological constructs, it is important to emphasize that possible uses of such biomarkers as measures should not lead us to confuse body-based concepts (the DRD4 gene, dopamine production) with actionbased ones (impulsivity), as the logically continuous accounts of social explanation require the latter.…”

Section: The Primacy Of Embodied Variationmentioning

confidence: 99%

Genetics and the Social Science Explanation of Individual Outcomes

Freese¹

2008

American Journal of Sociology

197

181

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Accumulating evidence from behavioral genetics suggests that the vast majority of individual-level outcomes of abiding sociological interest are genetically influenced to a substantial degree. This raises the question of the place of genetics in social science explanations. Genomic causation is described from a counterfactualist perspective, which makes its complexity plain and highlights the distinction between identifying causes and substantiating explanations. For explanation, genomic causes must be understood as strictly mediated by the body. One implication is that the challenge of behavioral genetics for sociology is much more a challenge from psychology than biology, and a main role for genetics is as a placeholder for ignorance of more proximate influences of psychological and other embodied variation. Social scientists should not take this challenge from psychology as suggesting any especially fundamental explanatory place for either it or genetics, but the contingent importance of genetic and psychological characteristics is itself available for sociological investigation.

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“…Especially from the standpoint of methodological individualism, sociologists have strongly criticized the supposition that statistical techniques can in themselves provide adequate causal explanations of social phenomena. Such techniques can show only relations among variables, and not how these relations are actually produced^as they can indeed only be produced^through the action and interaction of individuals (see Boudon, 1976Boudon, , 1987Coleman, 1986;Abbott, 1992;also Lindenberg and Frey, 1993;Esser, 1996;Swedberg, 1998a, 1998b).…”

Section: Causation As Robust Dependencementioning

confidence: 99%