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SCISIPBIO: Constructing Heterogeneous Scholarly Graphs to Examine Social Capital During Mentored K Awardees Transition to Research Independence: Explicating a Matthew Mechanism

SCISIPBIO：构建异质学术图来检验受指导的 K 获奖者向研究独立过渡期间的社会资本：解释马太机制

基本信息

批准号：
2122232
负责人：
Patricia Mabry
金额：
$ 99.51万
依托单位：
HEALTHPARTNERS Institute
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2122232&HistoricalAwards=false
关键词：
SCISIPBIO Constructing Heterogeneous Scholarly Graphs

项目摘要

Despite efforts at diversification, an outsized proportion of prestigious NIH R01 awards go to a circumscribed group of individuals and institutions. How and why does this happen? The Matthew Effect, whereby success begets success, is thought to be responsible: applicants with even small advantage at the outset may have their advantage multiplied many times over following initial success. Evidence is consistent with the presence of a Matthew Effect in R01 funding, yet no study has illuminated the specific nature of the advantage, nor detailed the means by which advantage is multiplied and accumulated. This project will answer questions about which aspects of social capital and scholarly achievement contribute most to R01 success, and whether gender or timing of scholarly events contribute, by examining the individual career trajectories of awardees of NIH Mentored Career Development Awards (MK awards). Project results will help design effective interventions to avert unintended funding disparities, while maintaining a rigorous peer review system. This will be the first empirical test of a Matthew Mechanism during transition to research independence and the first to leverage heterogeneous scholarly graphs (HSGs). The first aim is to capture complex relationships between each MK awardee, their scholarly achievement and social capital, and R01 success during their quest for research independence. Existing bibliographic and NIH award data will be combined in the construction of a “global” HSG database - relating all MK awardees to their associated scholarly objects. The result will be a comprehensive graph structured database in which nodes represent all MK awardees and their associated scholarly objects (e.g., published articles, journals, primary academic institution, coauthors, coauthor’s scholarly objects), and edges represent relationships of various types (e.g., author of, cited by, affiliation, research topics). Relationship context will be captured for all scholarly objects in the HSG through global, local, and hyper-local graphical feature extraction to comprehensively characterize MK awardees’ scholarly profiles. Second, survival models will be developed to predict R01 success for MK awardees from latent and observed variables of scholarly achievement and social capital, and global, local, and hyper-local HSG features. The study offers a novel approach to studying complex social processes that marries social capital theory with heterogeneous scholarly graphs and network science methods. This study will go beyond previous studies in providing a multidimensional characterization of scholarly social capital (beyond coauthorship and citation) and will examine differential social capital accumulation as a mediator in MK to R01 transition. Empirically-grounded predictive models will be designed to probe existing theory and yield insights on social capital’s role in R01 funding success. This study will yield actionable knowledge to inform strategies aimed at improving efficiency awards and increasing the diversity of the awardee pool.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.

尽管在多样化方面做出了努力，但享有盛誉的NIH R 01奖项中有很大一部分授予了有限的个人和机构。这是如何以及为什么发生的？马太效应，即成功带来成功，被认为是负责任的：申请人在一开始即使有很小的优势，也可能比最初的成功增加很多倍。证据与R 01资金中存在马太效应是一致的，但没有研究阐明了优势的具体性质，也没有详细说明优势倍增和积累的方式。该项目将通过研究NIH辅导职业发展奖（MK奖）获奖者的个人职业轨迹，回答有关社会资本和学术成就的哪些方面对R 01的成功贡献最大，以及性别或学术活动的时机是否有贡献的问题。项目成果将有助于设计有效的干预措施，避免意外的供资差距，同时保持严格的同行审查制度。这将是在向研究独立过渡期间对马修机制的第一次实证测试，也是第一次利用异质学术图（HSG）。第一个目标是捕捉每个MK获奖者，他们的学术成就和社会资本，和R 01的成功在他们追求研究独立之间的复杂关系。现有的书目和NIH奖数据将结合在一个“全球”HSG数据库的建设-所有MK获奖者与其相关的学术对象。结果将是一个全面的图形结构数据库，其中节点表示所有MK获奖者及其相关的学术对象（例如，出版的文章、期刊、主要学术机构、合著者、合著者的学术对象），并且边表示各种类型的关系（例如，作者，引用，从属关系，研究主题）。关系上下文将通过全球，本地和超本地的图形特征提取，以全面表征MK获奖者的学术概况在HSG中的所有学术对象捕获。第二，生存模型将被开发来预测R 01的成功MK获奖者从潜在的和观察到的变量的学术成就和社会资本，以及全球，本地和超本地HSG功能。这项研究提供了一种新的方法来研究复杂的社会过程，将社会资本理论与异质学术图表和网络科学方法结合起来。本研究将超越以往的研究，提供了一个多维的表征学术社会资本（超越合著和引用），并将研究不同的社会资本积累作为中介在MK到R 01过渡。基于经验的预测模型将被设计为探索现有的理论和产生的洞察力的作用，在R 01融资的成功。这项研究将产生可操作的知识，为旨在提高效率奖和增加获奖者池的多样性的战略提供信息。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。