Citation metrics are the best tools for research assessments. However, current metrics may be misleading in research systems that pursue simultaneously different goals, such as the advance of science and incremental innovations, because their publications have different citation distributions. We estimate the contribution to the progress of knowledge by studying only a limited number of the most cited papers, which are dominated by publications pursuing this progress. To field-normalize the metrics, we substitute the number of citations by the rank position of papers from one country in the global list of papers. Using synthetic series of lognormally distributed numbers, we developed the Rk-index, which is calculated from the global ranks of the 10 highest numbers in each series, and demonstrate its equivalence to the number of papers in top percentiles, P_top0.1% and P_top0.01% . In real cases, the Rk-index is simple and easy to calculate, and evaluates the contribution to the progress of knowledge better than less stringent metrics. Although further research is needed, rank analysis of the most cited papers is a promising approach for research evaluation. It is also demonstrated that, for this purpose, domestic and collaborative papers should be studied independently.

翻译：引用指标是科研评估的最佳工具。然而，当前指标在追求不同目标（如科学进步与增量创新）的科研体系中可能产生误导，因为这些体系的论文具有不同的被引分布。我们仅通过研究主导科学进步目标的高被引论文（即被引次数排名前端的少量论文）来估算其对知识进步的贡献。为对指标进行学科归一化，我们以论文在全球榜单中的排名位置替代其被引次数。利用对数正态分布数的合成序列，我们开发了Rk指数，该指数由每组序列中前10个最高值的全球排名计算得出，并证明了其与顶级百分比论文数（P_top0.1%和P_top0.01%）的等价性。在实际案例中，Rk指数计算简便，且相比宽松指标能更精准地评估对知识进步的贡献。尽管需进一步研究，但高被引论文的排名分析是一种极具潜力的科研评估方法。研究同时表明，在该评估中应独立分析本国论文与国际合作论文。