Incubators of Knowledge: Predicting protege productivity and impact in the social sciences

博士教育在塑造未来学者的职业生涯方面发挥着关键作用，从而对一个国家的知识创造轨迹产生影响。在博士课程期间，学生们会适应学科的规范，学习指导他们一生的学术实践和行为。导师作为这些学术流浪者的向导，是该学科的关键把关人，可以对他们的博士生产生深远的影响。博士生占学术队伍的较大比例，但学者们对他们在学术网络中的地位、他们对学术产出的贡献程度以及这种产出的影响知之甚少。很少有定量分析表明顾问的学术实践与其顾问未来的成功之间的关系。本研究从两个主要角度来研究这些问题：了解博士生对社会科学研究的贡献(这种贡献的程度和特点)和这项研究的影响(通过引文可见性)；检查导师的知识基础和知识传播实践，以及这些因素是否涉及扩大知识前沿，以及它们如何与博士生的职业轨迹和未来成功相关。这种方法允许量化顾问行为，并记录顾问行为中的模式。因此，这项研究提供了一个可行的框架，用于根据导师的素质和学生在博士学习过程中的发表实践来预测导师的成功。然后，科学政策制定者和管理人员可以使用这种预测模型来更有效地分配资源，并确定促进高等教育创新的方法。这项工作支持对博士生对知识创造的贡献以及导师的学术实践与其门生的生产力和影响力之间的关系的定量理解。这样的模式还将增强我们对顾问的不同科学实践--包括他们在特定学科中的嵌入性、不同学科之间的流动性程度以及参与高度协作的跨学科工作--将如何影响其顾问的职业轨迹和科学成功的理解。在这项关于ProteGES的第一项研究中，多个数据集和创新分析的结合为开发新的衡量标准和评估博士生教育中的知识传播、学术生产力和科学影响提供了丰富的基础。广泛的影响：通过资助学生和将该项目整合到课堂中来提供教育机会。此外，本研究还为博士研究生教育中的辅导与个体特征的互动提供了一个分析平台。这项工作的研究成果将在国家和国际会议和讲习班上传播。将详细说明匹配不同类型数据集的过程以及数据集本身，并在网上提供，以加强可复制性，并使其他科学家能够采用和推广这些方法。

Doctoral education plays a pivotal role in shaping the careers of future scholars and, thereby, in making an impact on the trajectory of knowledge creation in a nation. During a doctoral program, students are acculturated to the norms of the discipline, learning scholarly practices and behaviors that guide them for a lifetime. Advisors, as guides to these scholarly journeymen, serve as critical gatekeepers to the discipline and can have a profound influence on their doctoral students. Doctoral students comprise a larger portion of the academic workforce, yet scholars have very little knowledge of their place in scholarly networks, the degree to which they contribute to scholarly output, and the impact of this output. Very little quantitative analysis shows the relationship between advisors' scholarly practices and the future success of their advisees. This study investigates these issues from two main angles: understanding the contribution of doctoral students to social-science research (the extent and character of this contribution) and the impact of this research (visibility through citations); and examining the advisor's knowledge base and knowledge-diffusion practices, and whether these factors are involved in expanding knowledge frontiers and how they relate to the career trajectories and future success of doctoral students. This approach allows quantification of advisor behaviors and documentation of patterns within advisee behaviors. This research thus produces a viable framework for predicting advisee success based on advisor qualities and individual students' publication practices in the course of their doctoral studies. This predictive model can then be used by science policy makers and administrators for more efficient allocation of resources and to identify ways to promote innovation in higher education. This work supports a quantitative-based understanding of contribution of doctoral students to the creation of knowledge and of the relationship between the scholarly practices of advisors and the productivity and impact of their proteges. Such a model will also enhance our understanding of how the different scientific practices of the advisors -- including their embeddedness in particular disciplines, degree of mobility among different disciplines, and involvement in highly collaborative interdisciplinary work -- will have an effect on the career trajectories and scientific success of their advisees. The combination of multiple datasets and the innovative analyses in this first research study on proteges provides a rich foundation for the development of new metrics and evaluations of knowledge diffusion, scholarly productivity, and scientific impact in doctoral education.Broader Impacts: Educational opportunities are provided through the funding of students and the integration of this project into the classroom. In addition, this research provides a platform for future analyses on the interaction of mentoring in doctoral education with individual protege characteristics. The research products of this work will be disseminated at national and international conferences and workshops. The process of matching heterogeneous datasets, as well as the datasets themselves, will be detailed and made available online in order to enhance replicability and enable other scientists to adopt and expand these approaches.