Department of Computer Science, University of Colorado, Boulder, CO 80309;
Department of Computer Science, University of Colorado, Boulder, CO 80309.
Proc Natl Acad Sci U S A. 2017 Oct 31;114(44):E9216-E9223. doi: 10.1073/pnas.1702121114. Epub 2017 Oct 17.
A scientist may publish tens or hundreds of papers over a career, but these contributions are not evenly spaced in time. Sixty years of studies on career productivity patterns in a variety of fields suggest an intuitive and universal pattern: Productivity tends to rise rapidly to an early peak and then gradually declines. Here, we test the universality of this conventional narrative by analyzing the structures of individual faculty productivity time series, constructed from over 200,000 publications and matched with hiring data for 2,453 tenure-track faculty in all 205 PhD-granting computer science departments in the United States and Canada. Unlike prior studies, which considered only some faculty or some institutions, or lacked common career reference points, here we combine a large bibliographic dataset with comprehensive information on career transitions that covers an entire field of study. We show that the conventional narrative confidently describes only one-fifth of faculty, regardless of department prestige or researcher gender, and the remaining four-fifths of faculty exhibit a rich diversity of productivity patterns. To explain this diversity, we introduce a simple model of productivity trajectories and explore correlations between its parameters and researcher covariates, showing that departmental prestige predicts overall individual productivity and the timing of the transition from first- to last-author publications. These results demonstrate the unpredictability of productivity over time and open the door for new efforts to understand how environmental and individual factors shape scientific productivity.
一位科学家在其职业生涯中可能发表数十篇甚至数百篇论文,但这些贡献在时间上并不是均匀分布的。六十年来,对各种领域职业生产力模式的研究表明,存在一种直观且普遍的模式:生产力往往会迅速上升到早期高峰,然后逐渐下降。在这里,我们通过分析来自美国和加拿大的 205 个拥有博士学位授予权的计算机科学系的 2453 名终身教职员工的超过 20 万篇论文和招聘数据构建的个人教职员工生产力时间序列结构,来检验这一传统叙述的普遍性。与仅考虑部分教职员工或部分机构或缺乏共同职业参考点的先前研究不同,我们在这里将大型书目数据集与涵盖整个研究领域的职业转型的综合信息相结合。我们表明,无论系的声望或研究人员的性别如何,传统的叙述自信地仅描述了五分之一的教职员工,而其余五分之四的教职员工表现出丰富多样的生产力模式。为了解释这种多样性,我们引入了一个简单的生产力轨迹模型,并探讨了其参数与研究人员协变量之间的相关性,结果表明部门声望可以预测个人的整体生产力以及从第一作者到最后作者的论文发表的过渡时间。这些结果表明了生产力随时间的不可预测性,并为新的努力打开了大门,以了解环境和个人因素如何塑造科学生产力。