论文标题
折叠和附录：聚会是多么民粹主义者？使用监督的机器学习来衡量政党宣言中的民粹主义程度
Corrigendum and addendum to: How Populist are Parties? Measuring Degrees of Populism in Party Manifestos Using Supervised Machine Learning
论文作者
论文摘要
本文是先前发表的文章的陈列和附录：“当事人的民粹主义者如何？使用监督的机器学习来衡量政党宣言中的民粹主义程度”（政治分析，1-17。DOI：10.1017/PAN.2021.29）。这些腐败和附录已准备好纠正数据标记中的错误，并显示了先前发表的论文中未包含的一些额外见解。在这里，我们报告了这些更正，并指出了一些额外的结论，该结论专注于每个各方和年份的标签改组的效果，并在适当的情况下展示新的数字。我们表明，尽管在先前发表的文章中提出的简化标记方法可以引起与专家评分相关的偏见，但随机标记可显着降低相关性。我们表明，基于手动编码的数据集的相关性也是如此。这些修改基于其他证据和未来出版物中详细报告的结果。
This paper is a corrigendum and addendum to the previously published article: 'How Populist are Parties? Measuring Degrees of Populism in Party Manifestos Using Supervised Machine Learning' (Political Analysis, 1-17. doi:10.1017/pan.2021.29). These corrigendum and addendum were prepared to correct errors in data labelling and show some extra insights not included in the previously published paper. Here, we report these corrections and point to some additional conclusions by focusing on the effects of the label reshuffling per parties and years and presenting new figures wherever appropriate. We show that although the simplified labelling method proposed in the previously-published article can induce biases in the correlations with expert scores, random labelling reduces correlations significantly. We show that this is also true for correlations based on a manually-coded data set. These modifications are based on other evidence and results reported in detail in a future publication.
