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Organized Mobility and Relational Turnover as Context for Social Mechanisms: A Dynamic Invariant at the Heart of Stability from Movement

Abstract : This chapter offers a neostructural perspective on how organized mobility and relational turnover (OMRT) constitute important dimensions of the social context in which social mechanisms are deployed. They determine many of the characteristics of those mechanisms. As an illustration, White [HC. Chains of opportunity: system models of mobility in organizations. Harvard University Press, Cambridge, MA, (1970)] analysis of “mobility in loops” (p. 380) is combined with Snijders [TAB. “Models for longitudinal network data”. In: Carrington PJ, Scott J, Wasserman S (eds) Models and methods in social network analysis. Cambridge University Press, Cambridge, pp. 215−247, (2005)] models of network dynamics to look at how rotation across a carrousel of organizational places and subsequent relational turnover create a relational infrastructure that shapes a social process such as collective learning. Using a longitudinal study of advice networks among lay judges at the Commercial Court of Paris as an empirical example of collective learning, the author draws on a “spinning-top model” to account for the dynamics of these networks, in particular their cyclical centralization and decentralization over time, with OMRT in the Court providing the energy that drives this evolution and process. A “dynamic invariant” and its outcome, stability from movement, are thus identified at the heart of collective learning but are also shown to have an intrinsic multilevel character with consequences for catch-up dynamics between superimposed levels of agency. It is suggested that a neostructural perspective can thus inspire new collaborations between sociology and geography.
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Submitted on : Wednesday, May 10, 2017 - 3:26:50 PM
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Emmanuel Lazega. Organized Mobility and Relational Turnover as Context for Social Mechanisms: A Dynamic Invariant at the Heart of Stability from Movement. Knowledge and Networks, Springer, pp.119 - 142, 2017, 9783319450223. ⟨hal-01520522⟩

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