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• W2892027768 abstract "Significance Scientific communications about climate change are frequently misinterpreted due to motivated reasoning, which leads some people to misconstrue climate data in ways that conflict with the intended message of climate scientists. Attempts to reduce partisan bias through bipartisan communication networks have found that exposure to diverse political views can exacerbate bias. Here, we find that belief exchange in structured bipartisan networks can significantly improve the ability of both conservatives and liberals to interpret climate data, eliminating belief polarization. We also find that social learning can be reduced, and polarization maintained, when the salience of partisanship is increased, either through exposure to the logos of political parties or through exposure to political identity markers." @default.
• W2892027768 created "2018-09-27" @default.
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• W2892027768 date "2018-09-04" @default.
• W2892027768 modified "2023-10-17" @default.
• W2892027768 title "Social learning and partisan bias in the interpretation of climate trends" @default.
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• W2892027768 doi "https://doi.org/10.1073/pnas.1722664115" @default.
• W2892027768 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6166837" @default.
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• W2892027768 hasPublicationYear "2018" @default.
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