Although much extreme discourse and even vitriol is found at the poles, we still know little about how individuals make sense of immigration as “complicated” and even “too complex” to make sense of. Such issues are important to address if we are to better understand how individuals arrive at centrist viewpoints and see themselves as between the poles.
UC Berkeley Chancellor's Professor of Sociology G. Cristina Mora examines the issue by drawing on a unique survey of Californians and illustrative, linked, in-depth interviews. Using Latent Class Analysis, she identifies five distinct attitudinal classes, showing that three, constituting 40% of respondents, lie between consistently pro- and anti-immigrant stances.
These interviews provide rich insight into how those in the middle respondents rationalize their stances and express different forms of bias. Mora argues that rather than expressing undifferentiated views, those in the middle express distinct types of select uncertainty that, in turn, allows them to frame the issue differently. At an ideological level, selective uncertainty helps individuals to rationalize political commitments with outgroup bias toward the undocumented, while at a discursive level it allows them to distance themselves from the more biting right-wing views. Mora discusses the theoretical and methodological implications of her findings for understanding immigration attitudes as a field of positions and the relationship between uncertainty and “soft bias” more generally.
Lunch will be served. Please RSVP for the head count.
