The Department of Sociology and the Institute for Data Science invite you to join us for a seminar from Prof. Amir Goldberg of Stanford University.
A Language-Based Model of Organizational Identification Demonstrates how Within-Person Changes in Identification Relate to Network Position
Shifting attachments to social groups are a constant in the modern era. They are especially pronounced in the contemporary workplace. What accounts for variation in the strength of organizational identification? Whereas prior work has mostly focused on explaining variation between individuals, we develop a network-analytic theory of within-person changes in identification. We hypothesize that identification is positively related to occupying positions characterized by local clustering--having contacts who are mutually interconnected--and global bridging--having contacts who are disproportionately connected to individuals beyond a focal actor's direct reach. We use the tools of computational linguistics to develop a language-based measure of identification and find support for the theory using pooled data of internal communications from three disparate organizations.
Talk is scheduled for 12-1:15pm ET in room AA160, Arts & Admin Bldg, UTSC, on October 20. A light lunch will be served, and we encourage those attending in person to arrive a bit earlier to grab lunch and a seat. Doors will be open at 11:45am.
For those unable to attend in person:
Virtual link – Zoom: https://utoronto.zoom.us/j/88911252361,
Meeting ID: 889 1125 2361,
No registration required
Amir Goldberg’s research lies at the intersection of cultural sociology, data science and organization studies. He is interested in understanding how social meanings emerge and solidify through social interaction, and what role network structures play in this process. As co-director of the computational culture lab, Amir uses and develops computationally intensive network- and language-based methods to study how new cultural categories take form as people and organizational actors interact.