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中起着关键作用，从而对一个国家的知识创造轨迹产生影响。在攻读博士学位期间，学生们要适应学科的规范，学习指导他们一生的学术实践和行为。导师作为这些学者的向导，是学科的关键看门人，可以对他们的博士生产生深远的影响。博士生占学术劳动力的很大一部分，然而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。很少有定量分析显示导师的学术实践与被导师未来的成功之间的关系。本研究从两个主要角度探讨这些问题：理解博士生对社会科学研究的贡献（这种贡献的程度和特征）和研究的影响（通过引用的可见性）；考察导师的知识基础和知识传播实践，以及这些因素是否与拓展知识前沿有关，以及它们与博士生的职业轨迹和未来成功之间的关系。这种方法允许对顾问行为进行量化，并记录顾问行为中的模式。因此，这项研究产生了一个可行的框架，可以根据导师的素质和个别学生在博士研究过程中的出版实践来预测导师的成功。然后，科学政策制定者和管理者可以使用这个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。这项工作支持以定量为基础的理解博士生对知识创造的贡献，以及导师的学术实践与其门徒的生产力和影响之间的关系。这样的模型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的嵌入程度、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）如何影响其顾问的职业轨迹和科学成功的理解。在这首关于门徒的研究中，多个数据集的结合和创新分析为开发新的指标和评估博士教育中的知识传播、学术生产力和科学影响提供了丰富的基础。更广泛的影响：通过资助学生和将该项目融入课堂，提供了教育机会。此外，本研究也为未来博士教育中师徒关系与个体徒弟特征的互动分析提供了平台。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。匹配异构数据集的过程，以及数据集本身，将被详细描述并在网上提供，以增强可复制性，并使其他科学家能够采用和扩展这些方法。

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.