This Working Group is interested in issues of stigma, fear, discrimination and backlash in light of COVID-19, as well as social countermeasures and emergency management actions that can be taken to address them. The group’s broad scope includes research interest in xenophobia, stigma faced by frontline workers and those that have tested positive, compounded discrimination as experienced by those already marginalized, and the role played by culture and social media.
Resources / COVID-19 Resources / Working Groups / Issues, Impacts, and Recovery / Stigma, Fear, Discrimination, and Backlash in COVID-19
Stigma, Fear, Discrimination, and Backlash in COVID-19
Working Group Leads
Aaida Mamuji, York University and Angela Chia-Chen Chen, Arizona State University
Emails: amamuji@yorku.ca; Angela.CCChen@asu.edu
Working Group Members
Omer Aijazi, University of Toronto
Fatima Akter, University of Dhaka
Edris Alam, Rabdam Academy
Charissa S. L. Cheah, University of Maryland
Manomita Das, Birla Institute of Technology and Science
Myra F. Divina, University of Washington
Benjamin Goings, Walden University
Lesley Gray, University of Otago
Seungyong Han, Arizona State University
Sunny Kim, Arizona State University
Kevin Kupietz, Elizabeth City State University
Karen Leong, Arizona State University
Wei Li, Arizona State University
Mauricio Macias, Arizona State University
Keely Maxwell, U.S. Environmental Protection Agency
Kimberly A. Noels, University of Alberta
Lihong Ou, Arizona State University
Idrisa Pandit, Luther University College
Lori Peek, University of Colorado Boulder
Yerina S. Ranjit, University of Missouri
Jack Rozdilsky, York University
Duncan Sanderson, Independent Researcher
Anna Stefaniak, Carleton University
Yining Tan, Arizona State University
Michael J. A. Wohl, Carleton University
Dawit Yasin, York University
Weichao Yuwen, University of Washington
CONVERGE is funded by the National Science Foundation (NSF), Division of Civil, Mechanical, and Manufacturing Innovation, Program on Humans, Disasters, and the Built Environment (Award #1841338). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.