SOCIUM Forschungszentrum Ungleichheit und Sozialpolitik
Raum: 9.73280
Mary-Somerville-Straße 9
28359 Bremen
10:15 Uhr
Methodenveranstaltungen der Brückenprofessur
SoSe 2016

Dan McFarland is Professor of Sociology and Organizational Behavior at Stanford University. His research focuses on the social and organizational dynamics of educational systems like schools, classrooms and universities. In particular, Dan has performed a series of studies on classroom organization and interaction; on the formation of adolescent relationships, social structures, and identities; on interdisciplinary collaboration and intellectual innovation; and on relational sociology. His interdisciplinary collaborations with linguists and computer scientists are cutting-edge studies of big data and methodological advances in social networks and language modeling.

This paper attempts to directly consider the nature of relationships and the role of interaction dynamics more deeply. To this end, relationships are reconceptualized as a story between persons that is perceived (labeled), agreed upon, and enacted in interaction. From this perspective, types of ties like friendship are relational frameworks that are mutually recognized and enacted via certain interactional footings. To identify the effect of interactional footings over and above previously identified network mechanisms, we rely on systematic social observations of hundreds of settings that extend across one hundred thousand turns of social interaction, as well as longitudinally collected sociometric surveys and institutional records. With these data, interactions are not only coded for a variety of qualities, but they are situated in various social contexts and institutional framing efforts. For example, a particular interactional event, like the act of agreement between i and j at time t, can be embedded in a particular setting, a task (or sequence), a role-relation, and a reported friendship relation. Since most interactions are guided by any one or more of these framing efforts machine learning is employed to identify the interactions associated with each one while taking into account their overlap. Ultimately, the goal is to identify the interactional signal of a perceived and agreed upon reports of friendship. In such a fashion, we identify the interactional footings or ³friendship script² that actors employ to signal the relational frame of ³friendship². This signal - as a latent dimension - is then tested for its predictive capacity on friendship formation to ascertain if it has an effect over and above previously held mechanisms of tie formation.