通过专家知识启发加强句子严重性的测量

J. Pina-Sánchez, J. P. Gosling
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引用次数: 0

摘要

司法决策的定量研究面临着方法上的挑战,即分析以不同单位衡量的处置类型(例如罚款金额、监禁天数)。为了克服这一问题,文献中提出了广泛的句子严重性尺度。一组特殊的严重程度量表已经取得了很高的效度和信度是基于瑟斯通的两两比较。然而,这种方法需要一系列简化的假设,其中之一是不同处理类型所涵盖的严重性范围是恒定的。我们举办了一次专家启发讲习班,以评估这一假设的有效性。参加我们研讨会的六位刑法从业人员和研究人员的回答一致指出,不同处置类型的严重性范围差异很大(例如,缓刑的严重性范围比罚款的严重性范围大得多)。我们利用这些信息重新指定瑟斯通的模型,允许不相等的方差。结果,我们得到了一个新的、更可靠的判决严重性量表。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing the Measurement of Sentence Severity through Expert Knowledge Elicitation
Quantitative research on judicial decision-making faces the methodological challenge of analysing disposal types that are measured in different units (e.g. money for fines, days for custodial sentences). To overcome this problem a wide range of scales of sentence severity have been suggested in the literature. One particular group of severity scales that has achieved high validity and reliability are those based on Thurstone’s pairwise comparisons. However, this method invokes a series of simplifying assumptions, one of them being that the range of severity covered by different disposal types is constant. We undertook an expert elicitation workshop to assess the validity of that assumption. Responses from the six criminal law practitioners and researchers that participated in our workshop unanimously pointed at severity ranges being highly variable across disposal types (e.g. much wider severity ranges were identified for suspended custodial sentences than for fines). We used this information to re-specify Thurstone’s model allowing for unequal variances. As a result, we obtained a new, more robust, scale of sentence severity.
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