Production, Migration, & Differentiation: Analyzing the Co-Evolution of Careers & Knowledge Production

博士生在经济中，在科学和创新的行为中发挥着至关重要的作用;产业，政府和企业之间的知识转移;以及下一代科学家和工程师的教育。 有几项研究试图审查创新的产生、商业化和科学出版。然而，科学家和工程师的生产条件却鲜为人知。该项目研究了博士生培养的条件，以及这些条件如何与这些学生的职业轨迹和未来的研究和创新有关。该项目将提供有关有助于建立健全的科学环境的背景因素的基本知识。这些结果将用于STEM部门和项目，因为他们聘请教师，寻求行业合作伙伴关系，并制定博士培训政策。该项目研究了材料科学与工程领域的博士生培训，在个人，实验室和大学层面采用多层次的比较研究设计，具有三个相互关联的目标。首先，该项目开发了一个新的概念模型，用于描述实验室如何管理和组织研究。它结合了对研究生、教师和研究人员的采访，以及本地化的、大学随时可用的数据（如赞助项目数据、披露和年度报告），以分析不同的实验室管理模式是如何在实地工作的。其次，它研究了不同的实验室模型之间的关系和就业成果和研究生产力的个人博士学位获得者，采购数据，后者从公开可用的数据库。为了估计学生训练和解决内分泌问题的实验室中因素的因果效应，该项目使用了准实验样本设计和考虑样本选择的模型。第三，利用机器学习和计算文本分析的进步，该项目比较了实验室内和实验室之间的毕业生个人成果，并描述了毕业生的职业选择如何影响他们所做的研究的类型和内容。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响力审查标准进行评估，被认为值得支持。

Doctoral recipients represent a vital role in the economy, in the conduct of science and innovation; the transfer of knowledge between industry, government, and academe; and the education of the next generation of scientists and engineers. Several studies have sought to examine the production of innovation, commercialization, and publication of science. Less understood, however, are the conditions for the production of scientists and engineers. This project examines the conditions of doctoral training and how these conditions are related to career trajectories and future research and innovation of these students. The project will provide essential knowledge on the contextual factors which contribute to a robust scientific environment. These results will be of use to STEM departments and programs as they hire faculty, seek industry partnerships, and construct policies for doctoral training. This project examines doctoral training in the context of materials science and engineering utilizing a multi-level comparative research design at the individual, lab, and university levels with three interrelated goals. First, this project develops a novel conceptual model for characterizing how labs manage and organize research. It combines interviews with graduate students, faculty, and research staff with data that are localized and readily available to universities (such as sponsored projects data, disclosures, and annual reports) to analyze how different models of lab management work on-the-ground. Second, it examines the relationship between different lab models and the employment outcomes and research productivity of individual doctoral recipients, sourcing data for the latter from publicly available databases. To estimate the causal effect of the factors in the lab where students train and address issues of endogeneity, this project uses a quasi-experimental sample design and a model that accounts for sample selection. Third, taking advantage of advances in machine learning and computational text analysis, this project compares the outcomes of individual graduates within and across labs and describes how graduates' career choices impact the kind and content of research they produce.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.