论文标题

#MeToo运动对法院语言的影响 - 一种基于文本的因果推断方法

The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach

论文作者

Langen, Henrika

论文摘要

这项研究评估了#MeToo运动对51个美国州和联邦上诉法院的性暴力与性暴力相关案件的司法意见中使用的语言的影响。该研究介绍了各种指标,以量化法庭中的参与者采用隐式将责任从肇事者转移到受害者的语言的程度。一个指标可以衡量受害者被称为语法主题的频率,因为心理学领域的研究表明,将受害者归咎于受害者的责备越多,他们被称为语法主题。其他两个旨在评估受害者的水平的指数捕获了引用受害者和/或肇事者的句子中的情感和上下文。此外,司法意见被转变为词具和TF-IDF向量,以促进随着时间的流逝的语言演变的检查。 #MeToo运动的因果效应是通过差异性方法来估计的,该方法比较了对性犯罪和其他针对人的犯罪的语言发展以及小组事件研究方法的发展。该结果并未清楚地确​​定#元动作引起的语言变化,但表明该运动可能会稍微加速法庭的演变,从而导致效果具有很大的时间滞后。此外,该研究还考虑了在法官的性别和政治隶属关系方面的潜在影响异质性。该研究将因果推断与文本量化方法结合在一起,这些方法通常用于分类,以及依靠情感分析,单词嵌入模型和语法标记的指标。

This study assesses the effect of the #MeToo movement on the language used in judicial opinions on sexual violence related cases from 51 U.S. state and federal appellate courts. The study introduces various indicators to quantify the extent to which actors in courtrooms employ language that implicitly shifts responsibility away from the perpetrator and onto the victim. One indicator measures how frequently the victim is mentioned as the grammatical subject, as research in the field of psychology suggests that victims are assigned more blame the more often they are referred to as the grammatical subject. The other two indices designed to gauge the level of victim-blaming capture the sentiment of and the context in sentences referencing the victim and/or perpetrator. Additionally, judicial opinions are transformed into bag-of-words and tf-idf vectors to facilitate the examination of the evolution of language over time. The causal effect of the #MeToo movement is estimated by means of a Difference-in-Differences approach comparing the development of the language in opinions on sexual offenses and other crimes against persons as well as a Panel Event Study approach. The results do not clearly identify a #MeToo-movement-induced change in the language in court but suggest that the movement may have accelerated the evolution of court language slightly, causing the effect to materialize with a significant time lag. Additionally, the study considers potential effect heterogeneity with respect to the judge's gender and political affiliation. The study combines causal inference with text quantification methods that are commonly used for classification as well as with indicators that rely on sentiment analysis, word embedding models and grammatical tagging.

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