Meso level

The macro-micro divide has been on the foreground of the social science debate since its inception. While the macro-level identifies societal structures characterizing institutional and normative settings, the micro-level captures behavioral, cognitive, and emotional processes at the individual level. In this sense, the macro-level is synonymous of «global», «system» or «structure», opposed to the microscopic insight on the «actors», their «behaviors» or «agency» (Alexander, 1987). Emerging from this debate, more recently social scientists have proposed to differentiate between three interdependent micro-, meso- and macro-levels to improve understanding of an increasingly complex social world (Levy, 2012). In this framework, the term “meso” has been used to define intermediate units of analysis among economists, anthropologists, sociologists, criminologists or social psychologists. Although many epistemic differences emerge from this literature, network analysts seem to provide a consistent attempt to operationalize this notion for an interdisciplinary audience (Lazega & Snijders, 2015).

Definition and use across disciplines

What ‘in between’ means thus reflects differences across disciplines and approaches. Nevertheless, what social scientists who use this notion have most in common is their research of the processes where the interaction between micro and macro can be observed (Vacchiano & Spini, 2020). In this respect, Lazega and Snijders (2015) claim that network analysis has provided the most consistent attempt to address this issue. The reason is that by conceptualizing the emergence of social interactions as different types of networks, scholars can streamline the opportunities and constraints arising from social relationships as a juncture within the micro-macro gap. The reason is that one of the main assumptions of network theory is that social relationships can be studied as social contexts: that is, personal networks can be seen as aggregate units of analysis located at a level of social reality higher than individuals (Van Duijn, Van Busschbach & Snijders, 2009). Although individuals are constantly interwoven with other people during the flux of social life, networks are different from face-to face encounters, because they are constituted by a (more and less) stable set of participants. This means that networks follow a hierarchical structure, and thus, individuals are nested within enduring forms of social relationships that provide structural opportunities and constraints (Emirbayer, 1997; Bourdieu, 1986).

Network perspectives

What ‘in between’ means reflects differences across disciplines and approaches. Nevertheless, what social scientists who use this notion have most in common is their emphasis on considering contexts that are more proximate than macro-social ones, thus addressing individuals’ opportunities and constraints more concretely. In this respect, Lazega and Snijders (2015) claim that network analysis has provided the most consistent attempt to address this issue. The reason is that by conceptualizing the emergence of interactions as different types of networks, scholars can streamline the opportunities and constraints arising from social relationships as a juncture within the micro-macro gap. The reason is that one of the main assumptions of network theory is that social relationships can be studied as social contexts: ego, personal networks can be seen as aggregate units of analysis located at a level of social reality higher than individuals (Van Duijn, Van Busschbach and Snijders, 2009). Although individuals are constantly interwoven with other people during the flux of social life, networks are different from face-to face encounters, because they are constituted by a (more and less) stable set of participants. This means that networks follow a hierarchical structure, and thus, individuals are nested within enduring forms of relational opportunities and constraints at the meso-level (Emirbayer, 1997; Bourdieu, 1986) that potentially shape individual vulnerability .

Authors: Mattia Vacchiano, Dario Spini

References

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Semantic network visualisation

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