alex miltsov
Alex Miltsov
Position:
Assistant Professor
Office Phone:
(69) 4742
Website:
CV:

Research Interest

Biography
Selected Publications
Courses Offered

Alex Miltsov (PhD, McGill) is a sociologist, with specialization in work and occupations, social media and mass communication, deviance and criminology, and quantitative research methods. His larger research objective is to critically investigate the socio-economic and cultural effects of digital technology use and media representation. His studies have been supported by SSHRC graduate scholarships.

Alex’s work investigates several areas. First, he examines the use of digital technologies in the context of workplace resistance, time appropriation, and “slacking”. Combining cross-national surveying and in-depth interviewing, he analyzes how the digitization of the workplace affects workers’ experiences and interactions, their private and social lives, and their work/life balance.

A second line in his research involves a quantitative project, which examines the factors in sentencing outcomes for high-profile criminal cases. For this study, Alex created a unique dataset that allows to control for a variety of biographical, regional, and contextual factors in the analysis of sentencing outcomes for different socio-demographic groups in the U.S.

A third area of research involves a Big Data project on the extent and effects of gender- and race-based representations in print and digital media. As a result of this research, he has coauthored an article on the individual and structural factors explaining the persistent under-representation of women in print news. This article was published in 2015 in the American Sociological Review and won the 2017 CITAMS Best Paper Award.

Teaching:

Alex has extensive experience in teaching social science research methods and sociological perspectives. He also taught courses on deviance, socialization, urban social issues, and social movements. His current teaching interests include digital media sociology, work and occupations, and Big Data research methods.