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首页> 外文会议>Proceedings of the 77th ASISamp;T annual meeting, Connecting collections, cultures, and communities >The Dynamics of Social Capital in Scientific Collaboration Networks
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The Dynamics of Social Capital in Scientific Collaboration Networks

机译：科学合作网络中的社会资本动力学

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摘要

Scientific collaboration is considered a cornerstone of 21strnCentury Science and a springboard for economic prosperity.rnAt a more basic level, it is also considered to be arnfundamental part of the development of scientific humanrncapital. From that perspective, scientific collaboration isrnfacilitated through social capital, which is acquired throughrnan investment cycle. Through a series of collaborativerninteractions, scientists move to positions withinrncollaboration networks, which in turn creates futurernopportunities for collaboration.rnPrior research has either focused on the positioning ofrnscientists within their respective networks, or scientists’rnnetwork activity and career stage, but few studies havernlooked at the dynamics of positioning due to the presence,rntiming, and sequencing of collaborative interactions onrnfuture opportunities. The reason for this gap was thernintractability of the problem. In this paper, a researchrnproposal is outlined that will attempt to address thisrndeficiency by employing machine learning to identifyrnpatterns in the career trajectories of scientists in a researchrncommunity.
机译：科学合作被认为是21世纪世纪科学的基石和经济繁荣的跳板。在更基本的层面上，科学合作也被认为是科学人力资本发展的重要组成部分。从这个角度来看，通过社会资本促进了科学合作，而社会资本是通过投资周期获得的。通过一系列协作互动，科学家们转移到协作网络中的位置，从而为未来的协作创造了机遇。先前的研究要么专注于科学家在各自网络中的定位，要么关注科学家在网络活动和职业生涯中的位置，但是很少有人关注由于未来机会的协作互动的存在，时间安排和排序而产生的动态定位。出现这种差距的原因是该问题难以解决。本文概述了一项研究提案，该提案将尝试通过使用机器学习来识别研究社区中科学家的职业轨迹中的模式，以解决这一缺陷。

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