A discussion of measuring the top-1% most-highly cited publications: quality and impact of Chinese papers.

Autor/inn/en	Wagner, Caroline S.; Zhang, Lin; Leydesdorff, Loet
Titel	A discussion of measuring the top-1% most-highly cited publications: quality and impact of Chinese papers.
Quelle	In: Scientometrics, (2022) 4, S.1825-1839 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-022-04291-z
Schlagwörter	Citation analysis; National standing; International cooperation; Science; Technology; United States; China
Abstract	The top-1% 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% (PP-top1%) 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 China’s 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% of a sample of N papers is N / 100, ceteris paribus. Using the average citation rates as expected values, errors are introduced by (1) using the mean of highly skewed distributions and (2) 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; p < .05). Using percentile ranks, subsets other than indexing-based classifications can be tested for the statistical significance of differences among them. Graphical Abstract
Erfasst von	OLC
Update	2023/2/05