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Are You What You Read? Predicting Implicit Attitudes to Immigration Based on Linguistic Distributional Cues From Newspaper Readership; A Pre-registered Study

机译:你是你读的吗? 基于报纸读者的语言分布提示预测移民隐性态度; 预先注册学习

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The implicit association test (IAT) measures bias towards often controversial topics (e.g., race, religion), while newspapers typically take strong positive/negative stances on such issues. In a pre-registered study, we developed and administered an immigration IAT to readers of the Daily Mail (a typically anti-immigration publication) and the Guardian (a typically pro-immigration publication) newspapers. IAT materials were constructed based on co-occurrence frequencies from each newspapers’ website for immigration-related terms (migrant/immigrant) and positive/negative attributes (skilled/unskilled). Target stimuli showed stronger negative associations with immigration concepts in the Daily Mail compared to the Guardian, and stronger positive associations in the Guardian corpus compared to the Daily Mail corpus. Consistent with these linguistic distributional differences, Daily Mail readers exhibited a larger IAT bias, revealing stronger negative associations to immigration concepts compared to Guardian readers. This difference in overall bias was not fully explained by other variables, and raises the possibility that exposure to biased language contributes to biased implicit attitudes.
机译:隐性关联测试(IAT)衡量频繁的争议主题(例如,种族,宗教),而报纸通常对这些问题采取强大的正/负面立场。在预先注册的研究中,我们开发并管理了向日常邮件(通常反移民出版物)和监护人(典型的前移民出版物)报纸的读者的移民局。 IAT材料是根据来自每个报纸网站的共同发生频率为移民有关的术语(移民/移民)和正/负属性(熟练/非熟练)。与监护人相比,目标刺激与日常邮件中的移民概念更强的负面关联,与日常邮件语料库相比,监护语法中的更强大的积极协会。与这些语言分布差异一致,日常邮件读者展示了较大的IAT偏差,与监护人读者相比,移民概念的负面关联更强。整体偏差的这种差异没有得到其他变量的完全解释,并提高了暴露于偏置语言的可能性有助于偏见隐含态度。

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