论文标题
关于衡量最高1%的最高引用出版物的讨论:中国论文的质量和影响
A discussion of measuring the top-1 percent most-highly cited publications: Quality and impact of Chinese papers
论文作者
论文摘要
最高的1%最引人注目的文章被视为科学的先锋。使用Web of Science数据,人们可以发现,在2015年胜过欧盟的欧盟之后,中国在2019年的相对参与中已经超过了美国。但是,这一发现与对西方机构的反复报道形成了鲜明的对比,即西方机构的质量是,中国在科学中的质量也落后于其他高级国家,即使在艺术品数量中也陷入了困境。此处介绍的结果与先前的结果之间的差异主要取决于现场归一化,该域正常化通过纪律对源期刊进行了分类。这些子集的平均引文率通常用作基线,因此可以在学科之间进行比较。但是,N纸样本的前1%的预期值为N 100,Ceteris Paribus。使用平均引文率作为预期值,通过使用高度偏斜的分布的平均值以及在子集的描述中的奇异精度来引入错误。分类可用于分解,但不能用于归一化。当数据分解时,美国在病毒学等生物医学领域中排名领先。尽管论文数量较小,但在P小于.05时,中国在社会科学引文指数中的商业和金融领域的表现都胜过美国。使用百分位等级,可以测试除基于索引的分类以外的其他子集,以确保它们之间差异的统计意义。
The top 1 percent most highly cited articles are watched closely as the vanguards of the sciences. Using Web of Science data, one can find that China had overtaken the USA in the relative participation in the top 1 percent in 2019, after outcompeting the EU on this indicator in 2015. However, this finding contrasts with repeated reports of Western agencies that the quality of Chinese output in science is lagging other advanced nations, even as it has caught up in numbers of articles. The difference between the results presented here and the previous results depends mainly upon field normalizations, which classify source journals by discipline. Average citation rates of these subsets are commonly used as a baseline so that one can compare among disciplines. However, the expected value of the top 1 percent of a sample of N papers is N 100, ceteris paribus. Using the average citation rates as expected values, errors are introduced by using the mean of highly skewed distributions and a specious precision in the delineations of the subsets. Classifications can be used for the decomposition, but not for the normalization. When the data is thus decomposed, the USA ranks ahead of China in biomedical fields such as virology. Although the number of papers is smaller, China outperforms the US in the field of Business and Finance in the Social Sciences Citation Index when p is less than .05. Using percentile ranks, subsets other than indexing based classifications can be tested for the statistical significance of differences among them.