(错误)判断普通意义?语料库语言学、频率谬误与“一般意义”文本主义的外延-抽象区分

IF 0.2 Q4 LAW
Shlomo Klapper
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引用次数: 0

摘要

摘要罕见是一种新的法律意义的衡量标准。但在过去的十年里,语料库语言学已经开始作为一种新的工具来衡量法定解释中的普通意义和宪法解释中的原始公共意义。语料库语言学的法律应用认为,与词典、直觉或对来源的非系统调查相比,对各种文件中一个术语的每一次使用进行检查,可以对一个词产生更完整、更公正的理解。在法律分析中,语料库可以补充甚至取代词典和母语人士的直觉。特别是对于原创主义,法律语料库语言学承诺为迄今为止缺乏的观点提供一种更科学的方法。然而,语料库语言学在应用于法律问题时,却遭到了一种致命的方法论批评——频率谬误。批评指出,在语料库中,一个不寻常的意义可能有许多语料库条目,而一个完全普通的意义可能完全不在语料库中。也就是说,频率不是衡量意义的好方法。由于法律语料库语言学依赖于频率,语料库不能告知法律含义。这篇文章回避了这种致命的批评。它认为,虽然频率谬误是不言而喻的,但这种谬误并不是语料库固有的,而是将语料库视为词典来曲解语料库的产物。这种防御包括许多步骤。第一步区分两种不同的辨别普通意义的方法:扩展和抽象。正如耶茨诉美国案和美国诉马歇尔案所示,扩展意味着将法定术语扩展到不同的事实,而抽象则保持事实不变,并抽象出关键品质以找到合适的术语。至关重要的是,本文认为抽象提供了一种避免频率谬误的方法。第二,要正确使用抽象,不仅要分析有问题的法律术语的存在,还要分析它的不存在;也就是说,必须确定是否存在其他术语来描述类似的事实场景,以区分语言产物和世界事实。本文最后认为,这种方法具有有益的突发性。这个答案不仅使法律语料库分析在方法上合理,而且为第一个近似普通人如何阅读法律的工具铺平了道路,从而有可能进一步推进法治。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
(Mis)judging Ordinary Meaning?: Corpus Linguistics, the Frequency Fallacy, and the Extension-Abstraction Distinction in “Ordinary Meaning” Textualism
Abstract Rarely is a new yardstick of legal meaning created. But over the past decade, corpus linguistics has begun to be utilized as a new tool to measure ordinary meaning in statutory interpretation and original public meaning in constitutional interpretation. The legal application of corpus linguistics posits that an examination of every use of a term in a wide variety of documents can yield a more complete, impartial understanding of a word than can dictionaries, intuition, or an unsystematic survey of sources. Corpora could supplement, or even supplant, dictionaries and native-speaker intuition in legal analyses. For originalism in particular, legal corpus linguistics promises to offer what would be a more scientific methodology for a point of view which, until now, has lacked one. However, corpus linguistics, as applied to legal problems, falls prey to a fatal methodological criticism – the frequency fallacy. The criticism states that in a corpus, an unusual meaning can have many corpus entries while a perfectly ordinary meaning can be completely absent from the corpus. That is, frequency is not a good measure of meaning. Since legal corpus linguistics relies on frequency, the corpus cannot inform legal meaning. This article parries this otherwise fatal critique. It argues that while the frequency fallacy is self-evidently true, the fallacy is not inherent to the corpus, but rather is an artifact of misinterpreting the corpus by treating it like a dictionary. This defense consists of a number of steps. The first step distinguishes between two different methods of discerning ordinary meaning: extension and abstraction. As illustrated by Yates v. United States and United States v. Marshall, extension entails extending the statutory term to varying facts, while abstraction keeps the facts constant and abstracts out key qualities to find an appropriate term. Critically, this article argues that abstraction offers a way to avoid the frequency fallacy. Second, to use abstraction properly, one must analyze not only the presence of the legal term in question but also its absence; that is, one must determine the presence or absence of other terms to describe a similar factual scenario to distinguish between artifacts of language and facts about the world. This article concludes by arguing that this method has a beneficial emergent quality. Not only does this answer make legal corpus analysis methodologically sound, but it also paves the way for the first tool to approximate how an ordinary person would read the law, thus potentially furthering the rule of law.
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来源期刊
CiteScore
0.10
自引率
0.00%
发文量
6
审稿时长
18 weeks
期刊介绍: The British Journal of American Legal Studies is a scholarly journal which publishes articles of interest to the Anglo-American legal community. Submissions are invited from academics and practitioners on both sides of the Atlantic on all aspects of constitutional law having relevance to the United States, including human rights, legal and political theory, socio-legal studies and legal history. International, comparative and interdisciplinary perspectives are particularly welcome. All submissions will be peer-refereed through anonymous referee processes.
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