This paper calibrates how metrics derivable from the SAO/NASA Astrophysics Data System can be used to estimate the future impact of astronomy research careers and thereby to inform decisions on resource allocation such as job hires and tenure decisions. Three metrics are used, citations of refereed papers, citations of all publications normalized by the numbers of co-authors, and citations of all first-author papers. Each is individually calibrated as an impact predictor in the book Kormendy (2020), "Metrics of Research Impact in Astronomy" (Publ Astron Soc Pac, San Francisco). How this is done is reviewed in the first half of this paper. Then, I show that averaging results from three metrics produces more accurate predictions. Average prediction machines are constructed for different cohorts of 1990-2007 PhDs and used to postdict 2017 impact from metrics measured 10, 12, and 15 years after the PhD. The time span over which prediction is made ranges from 0 years for 2007 PhDs to 17 years for 1990 PhDs using metrics measured 10 years after the PhD. Calibration is based on perceived 2017 impact as voted by 22 experienced astronomers for 510 faculty members at 17 highly-ranked university astronomy departments world-wide. Prediction machinery reproduces voted impact estimates with an RMS uncertainty of 1/8 of the dynamic range for people in the study sample. The aim of this work is to lend some of the rigor that is normally used in scientific research to the difficult and subjective job of judging people's careers.
翻译:本文校准了从SAO/NASA天体物理学数据系统(SAO/NASA天体物理学数据系统)得出的衡量标准如何可用于估算天文学研究职业的未来影响,从而为资源分配决策提供依据,如聘用和任期决定等。使用了三个衡量标准,引用了参考论文,引用了所有出版物的引文,按合著者人数进行了标准化,并引用了所有第一著论文。在Kormendy(20202020年)的著作《天文学研究影响模型》(Publ Astron Soc Pac, 旧金山)中,每个都单独校准了影响预测。通常在本文前半部分中审查这项工作。然后,我表明,三个衡量标准的平均结果产生了更准确的预测。平均预测机器是为1990-2007年不同组群制作的,并用共同作者人数和博士后15年的衡量标准对2017年的影响。 预测的时间范围从2007年的博士的0年到1990年的博士的17年,使用经博士测量的十年后测量的数学。在17年的大学周期中,对2017年的货币周期中,对2017年的货币周期中,人们对2017年的大学周期的高度的预测进行了高度分析,对2017年的估算进行了高度进行高度的预测。