CDI-Type II: What drives the dynamic creation of science?

这个多学科项目借鉴了社会学，计算机科学和语言学，研究思想是如何通过科学社区创建和传播的，这些社区是如何形成和随着时间的推移而变化的，以及跨越这些社区的多学科网络如何塑造科学创新。它创建了复杂的新计算模型，用于提取和表示想法并衡量其影响和新奇，以及提取和表示社会关系并确定多学科合作的形式。它的方法将社会科学的网络分析工具与计算机科学的语言处理工具相结合。它使用网络分析来提高计算工具识别科学文本中思想的能力，以及计算语言学的工具来帮助解释科学合作和创新的共同进化。思想及其传播的模型可以用来研究假设，如多学科研究是否加速或减速科学创新，以及多学科如何影响学生和教师的职业生涯。对MEDLINE语料库的一个相对浅但大规模的研究与对斯坦福大学教师的更丰富的组织层面的研究相结合（他们的出版物，赠款，隶属关系，咨询和教学），以探索和分析创新和多学科合作的复杂相互关系。该项目的研究议程是产生新的和独特的数据，创造新的计算工具，并扩展理论，使学者们更普遍地改变他们对科学创新、多学科性和研究共同体的概念。社会网络和自然语言处理技术的整合有助于发展计算社会科学的新研究方向，同时为科学社会学提供经验的严谨性和规模，并将自然语言处理从以前的工程重点扩展到真正的解释性社会科学模型。

This multidisciplinary project draws upon sociology, computer science, and linguistics to study how ideas are created and propagate through scientific communities, how these communities are formed and change over time, and how multidisciplinary networks spanning these communities shape scientific innovation. It creates sophisticated new computational models for extracting and representing ideas and measuring their impact and novelty, and for extracting and representing social relations and identifying forms of multidisciplinary collaboration. Its methods integrate the network analytic tools of social science with the language processing tools of computer science. It uses network analysis to improve the ability of computational tools to identify ideas in scientific texts, as well as the tools of computational linguistics to help explain the co-evolution of scientific collaborations and innovations. The models of ideas and their diffusion can be used to investigate hypotheses such as whether multidisciplinary research accelerates or decelerates scientific innovation, and how multidisciplinarity influences student and faculty careers. A relatively shallow but large-scale study of the MEDLINE corpus is combined with a richer organization-level study of Stanford University faculty (their publications, grants, affiliations, advising, and teaching) in order to explore and analyze the complex interrelationships of innovation and multidisciplinary collaboration.The research agenda of this project is to produce new and unique data, create new computational tools, and extend theory so that scholars change their conceptions of scientific innovation, multidisciplinarity, and research communities more generally. The integration of social network and natural language processing techniques is helping to develop a new vein of research in computational social science, simultaneously offering empirical rigor and scale to the sociology of science and extending natural language processing from its previous engineering focus toward true explanatory social science models.