What Lies between the Poles? Selective Uncertainty and Occluded Bias in Immigration Attitudes in California

Abstract Although much extreme discourse is found at the poles, we still know little about how individuals in the center 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 the contemporary landscape of bias and belonging and the character of attitudes in the middle. We examine the issue by drawing on a unique survey of Californians and illustrative, linked, in-depth interviews. Using latent class analysis we identify five distinct attitudinal classes, showing that three, constituting 40 percent of respondents, lie between consistently pro- and anti-immigrant stances. Our interviews reveal that rather than expressing undifferentiated views, those in the middle express patterned forms of selective uncertainty that, in turn, allow them to frame the issue differently. At an ideological level, selective uncertainty helps individuals to narrow the scope of immigration in terms of what is determinable, and thus rationalize political commitments with outgroup bias toward the undocumented. At a discursive level, selective uncertainty affords an opportunity to soften or occlude bias and create a distance from the poles, especially on the right. Taken together, our findings open up the “black-boxed” middle of immigration attitudes to reveal its distinct categorical characteristics and show how selective uncertainty allows individuals to make sense of their positions. We discuss the theoretical and methodological implications of our findings, including for understanding immigration attitudes as a field of positions more generally.

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Publication Details

Journal
Social Forces
Published
2025-05-28
DOI
https://doi.org/10.1093/sf/soaf059
Citations
1
Primary Topic
Migration, Refugees, and Integration
Type
article
Field-Weighted Citation Impact
4.20

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article

What Lies between the Poles? Selective Uncertainty and Occluded Bias in Immigration Attitudes in California

Tianna S. Paschel, G. Cristina Mora, Chelsea Daniels
1 citations
Social Forces
Migration, Refugees, and Integration
4.20
article

What Lies between the Poles? Selective Uncertainty and Occluded Bias in Immigration Attitudes in California

Tianna S. Paschel, G. Cristina Mora, Chelsea Daniels
article en
1 citations

Abstract

Abstract Although much extreme discourse is found at the poles, we still know little about how individuals in the center 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 the contemporary landscape of bias and belonging and the character of attitudes in the middle. We examine the issue by drawing on a unique survey of Californians and illustrative, linked, in-depth interviews. Using latent class analysis we identify five distinct attitudinal classes, showing that three, constituting 40 percent of respondents, lie between consistently pro- and anti-immigrant stances. Our interviews reveal that rather than expressing undifferentiated views, those in the middle express patterned forms of selective uncertainty that, in turn, allow them to frame the issue differently. At an ideological level, selective uncertainty helps individuals to narrow the scope of immigration in terms of what is determinable, and thus rationalize political commitments with outgroup bias toward the undocumented. At a discursive level, selective uncertainty affords an opportunity to soften or occlude bias and create a distance from the poles, especially on the right. Taken together, our findings open up the “black-boxed” middle of immigration attitudes to reveal its distinct categorical characteristics and show how selective uncertainty allows individuals to make sense of their positions. We discuss the theoretical and methodological implications of our findings, including for understanding immigration attitudes as a field of positions more generally.

Social ForcesVol. 104(2)
Berkeley College (US), New York University (US), University of California, Berkeley (US)
Russell Sage Foundation, Sage Foundation, Johns Hopkins University, University of Notre Dame
Openalex Percentile: Top 8%
Migration, Refugees, and Integration
4.20
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