[目的/意义]分析学科规范引文影响力在科学评价中的可行性及其与同行评议的相关性，为负责任计量及以其为支撑的同行评议提供借鉴。[方法/过程]选取F1000以及InCites平台，将29 850篇细胞生物学文献、30 326篇生物技术文献的CNCI （学科规范化引文影响力）与被引频次进行相关分析，对其中956篇细胞生物学论文的CNCI与F1000分值进行斯皮尔曼相关系数检验。[结果/结论]研究结果表明，从统计学视角看CNCI与被引频次呈高度正相关，与F1000分值呈显著正相关，同时亦存在二者相悖的情形。因此，CNCI在一定程度上能够反映同行评议结果、能代偿实施学术影响力归誉的功能，并适用于跨学科比较；但同行评议或CNCI单独作为科学评价标准都会有失偏颇，以CNCI为代表的新一代负责任计量指标为支撑的同行评议将成为未来科学评价的主流。
[Purpose/significance] In order to make some recommendations for responsible metrics and peer review, the paper analyses the feasibility of Category Normalized Citation Impact(CNCI) and its correlation with peer review.[Method/process] By choosing F1000 and InCites, this paper conducted the correlation analysis between CNCI and citation counts of 29,850 cell biology papers and 30,326 biotechnology papers, and performed the test of spearman rank correlation coefficient between the CNCI and F1000 scores of 956 cell biology papers.[Result/conclusion] The results show that CNCI is positively correlated with citation counts, and significantly correlated with F1000 rating, but there are contradictory situations for the two indicators at the same time. Therefore, CNCI can reflect the results of peer review, substitute for the indication of academic influence, and also can be used for comparision among disciplines. However, peer review or CNCI will be biased for scientific evaluation purpose alone. Then, tools that link responsible metrics indicators, such as CNCI and informed peer reviews would be valuable additions to the field of research evaluation.
[1] WILSDON J. The metric tide:report of the independent review of the role of metrics in research assessment and management[M]. London:SAGE Publishing,2016:137-158.
[2] GARFIELD E. Citation indexing-its theory and application in science, technology, and humanities[M].New York:John Wiley & Sons,1979.
[3] HICKS D, WOUTERS P, WALTMAN L. Bibliometrics:the Leiden Manifesto for Research metrics[J]. Nature. 2015,520(7548):429-431.
[4] HAUNSCHILD R, BORNMANN L. Normalization of Mendeley reader counts for impact assessment[J]. Journal of informetrics, 2016, 10(1):62-73.
[5] TRACZ V, LAWRENCE R. Towards an open science publishing platform[EB/OL].[2018-07-20]. https://f1000researchdata.s3.amazonaws.com/manuscripts/8575/0c0c20d6-8920-4d1c-b564-e81901b268f4_7968_-_rebecca_lawrence.pdf?doi=10.12688/f1000research.7968.1.
[6] InCites[EB/OL].[2018-04-17].https://clarivate.com.cn/products/incites/.
[7] LEYDESDORFF L, RADICCHI F, BORNMANN L, et al. Field-normalized impact factors (IFs):A comparison of rescaling and fractionally counted IFs[J]. Journal of the American Society for Information Science & Technology, 2013, 64(11):2299-2309.
[8] BORNMANN L, HAUNSCHILD R. Measuring field-normalized impact of papers on specific societal groups:an altmetrics study based on Mendeley data[J]. Research evaluation, 2016, 26(3):10-35.
[9] InCites[EB/OL].[2017-06-03].https://incites.thomsonreuters.com/.
[10] F1000[EB/OL].[2017-06-05].http://f1000.com/prime.
[11] BORNMANN L, HAUNSCHILD R. Relative Citation Ratio (RCR):an empirical attempt to study a new field-normalized bibliometric indicator[J]. Journal of the Association for Information Science & Technology, 2017, 68(4):1064-1067.
[12] DERRICK G E, PAVONE V. Democratising research evaluation:achieving greater public engagement with bibliometrics-informed peer review[J]. Science & public policy, 2013, 40(5):563-575.
