基于引文分析的科学家投入产出绩效算法研究

Modeling of Input-output Performance of Scientists Based on the Analysis of Citation

该文提出了一种考虑投入和产出的科学家绩效算法。考虑到科学家的沟通、时间等投入成本,该算法以科学论文中目标科学家的合作作者数和机构数作为输入变量,以合作发表的文章及其被引数作为输出变量。基于输入和输出数据,建立科学家投入产出绩效评价模型。在实证数据上的实验结果显示,相对于发表文章数、总引用量、I10指数和H指数等指标,该方法可以更准确地识别出获诺贝尔奖的科学家,算法的AUC值为0.7957,比总引用量指标的准确度提高了8.77%。此外还发现大部分科学家获奖前的投入产出绩效高于获奖后科学家的投入产出绩效。该工作对科学地评价科学家的绩效具有重要意义。

This paper presents a model to evaluate input-output performance of scientists.With consideration of the input cost of scientists'communication and time,this model takes the number of co-authors and the number of institutions of target scientists in scientific papers as input variables,and the number of co-published articles and their cited number as output variables.The experiments results show the scientists who won Nobel Price are ranked higher than the sciences who did not win Nobel Price.The experimental results also show that the AUC values of input-output performance model could reach 0.7957 for the APS data set,which is better than the results generated by h-index,i10-index,total number of papers,and total number of citations.Furthermore,The experimental results indicate that most input-output performances of scientists before winning award is higher than the input-output performances of scientists after winning award for the APS data set and the web of science data set.The proposed model also provides an effective tool for policy makers to quantify the input-output performances of sciences.