关于欧洲激进和极端主义态度的数据集。

IF 7.2 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Sebastian Jungkunz, Nina Fadarkhan Osenbrügge, Marc Helbling
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引用次数: 0

摘要

该数据集介绍了第一个跨国家验证的左翼和右翼激进主义以及一般政治极端主义的测量量表,以及西方世界隐性政治极端主义的内隐联想测试(IAT)。它包括了从2022年到2024年在法国、德国、英国、希腊、匈牙利、荷兰和美国进行的十项调查中收集的18000多名参与者的数据。该数据集有助于对不同政治制度下激进和极端主义态度的起源和后果进行比较分析。此外,通过将态度测量与行为指标(如政党支持)联系起来,它为研究潜在的极端主义形式如何转化为政治行动提供了基础。除了极端主义研究之外,该数据集还有助于更广泛地研究民主弹性、政治两极分化和意识形态信仰结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A dataset on radical and extremist attitudes in Europe.

This dataset introduces the first cross-nationally validated measurement scales for left-wing and right-wing radicalism, and general political extremism, along with an implicit association test (IAT) for implicit political extremism in the Western world. It includes data from more than 18,000 participants collected through ten surveys conducted between 2022 and 2024 in France, Germany, Great Britain, Greece, Hungary, the Netherlands, and the United States. The dataset facilitates comparative analyses of the origins and consequences of radical and extremist attitudes across different political systems. Moreover, by linking attitudinal measures with behavioral indicators such as party support, it provides a foundation for examining how latent forms of extremism are translated into political action. Beyond extremism research, the dataset contributes to the broader study of democratic resilience, political polarization, and ideological belief structures.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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