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文本匹配软件相似度得分的使用。

The use of text-matching software's similarity scores.

作者信息

Manley Stewart

机构信息

Faculty of Law, University of Malaya, Kuala Lumpur, Malaysia.

出版信息

Account Res. 2023 May;30(4):219-245. doi: 10.1080/08989621.2021.1986018. Epub 2021 Oct 17.

Abstract

Popular text-matching software generates a percentage of similarity - called a "similarity score" or "Similarity Index" - that quantifies the matching text between a particular manuscript and content in the software's archives, on the Internet and in electronic databases. Many evaluators rely on these simple figures as a proxy for plagiarism and thus avoid the burdensome task of inspecting the longer, detailed Similarity Reports. Yet similarity scores, though alluringly straightforward, are never enough to judge the presence (or absence) of plagiarism. Ideally, evaluators should always examine the Similarity Reports. Given the persistent use of simplistic similarity score thresholds at some academic journals and educational institutions, however, and the time that can be saved by relying on the scores, a method is arguably needed that encourages examining the Similarity Reports but still also allows evaluators to rely on the scores in some instances. This article proposes a four-band method to accomplish this. Used together, the bands oblige evaluators to acknowledge the risk of relying on the similarity scores yet still allow them to ultimately determine whether they wish to accept that risk. The bands - for most rigor, high rigor, moderate rigor and less rigor - should be tailored to an evaluator's particular needs.

摘要

流行的文本匹配软件会生成一个相似度百分比,称为“相似度得分”或“相似性指数”,它量化了特定稿件与软件存档、互联网及电子数据库中的内容之间的匹配文本。许多评估者将这些简单数字作为抄袭的替代指标,从而避免了检查冗长、详细的相似度报告这项繁重的任务。然而,相似度得分尽管看似简单直接,但绝不足以判断是否存在抄袭。理想情况下,评估者应始终查看相似度报告。然而,鉴于一些学术期刊和教育机构持续使用简单化的相似度得分阈值,且依靠这些得分可以节省时间,因此可以说需要一种方法,既能鼓励查看相似度报告,又能在某些情况下允许评估者依赖这些得分。本文提出了一种四档方法来实现这一点。这些档次共同使用时,会迫使评估者认识到依赖相似度得分的风险,但仍允许他们最终决定是否愿意接受该风险。这些档次——最高严谨度、高严谨度、中等严谨度和低严谨度——应根据评估者的特定需求进行调整。

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