The University of Chicago Law Review ( IF 1.9 ) Pub Date : 2021-11-01 Fred R. Shapiro
This Essay presents a list of the fifty most-cited legal scholars of all time, intending to spotlight individuals who have had a very notable impact on legal thought and institutions. Because citation counting favors scholars who have had long careers, I supplement the main listing with a ranking of the most-cited younger legal scholars. In addition, I include five specialized lists: most-cited international law scholars, most-cited corporate law scholars, most-cited scholars of critical race theory and feminist jurisprudence, most-cited public law scholars, and most-cited scholars of law and social science. (For those readers who cannot wait to see the actual lists, Tables 1–7 are on pages 8–11.)
The utility of citation totals as indicators of scholarly quality or even of scholarly influence is controversial, but they have been shown to correlate positively with informed subjective assessments. The danger in relying on such counts is that, because they are so convenient, they will be disproportionately relied upon relative to their actual probative value. There are a number of significant biases in citation statistics, and there are a variety of pitfalls that should be avoided in attempting to compile meaningful citation data. I will describe these biases and pitfalls when I explain the derivation and methodology of my study. It is my hope that I have produced tabulations that, although they clearly have imperfections, can serve as examples of careful analysis. Such examples are sorely needed after flawed proposed “scholarly impact rankings” by the U.S. News and World Report threatened to have a harmful effect on legal education.
中文翻译:
重温被引用次数最多的法律学者
这篇文章列出了有史以来被引用次数最多的 50 位法律学者,旨在突出对法律思想和制度产生非常显着影响的个人。因为引用计数有利于拥有长期职业的学者,所以我用被引用次数最多的年轻法律学者的排名来补充主要列表。此外,我还列出了五个专业列表:被引用最多的国际法学者、被引用最多的公司法学者、被引用最多的批判种族理论和女权法学学者、被引用最多的公法学者和被引用最多的法律和法律学者。社会科学。(对于那些迫不及待想看实际列表的读者,表 1-7 位于第 8-11 页。)
引用总数作为学术质量甚至学术影响的指标是有争议的,但它们已被证明与知情的主观评估呈正相关。依赖这些计数的危险在于,由于它们非常方便,相对于它们的实际证明价值,它们将被过度依赖。引文统计中存在许多重大偏差,并且在尝试编制有意义的引文数据时应避免各种陷阱。当我解释我的研究的推导和方法时,我将描述这些偏见和陷阱。我希望我制作的表格虽然明显有缺陷,但可以作为仔细分析的例子。