Collaborative Research: Scaling Insight into Science: Assessing the value and effectiveness of machine assisted classification within a statistical system

该项目开发和比较了美国科学研究分类和分析的前沿方法，并将它们与现有的人工生成方法进行了比较。该项目考察了四种计算方法的优缺点：主题模型、基于网络的划分方法、基于维基百科的标注和主动学习方法。特别是，该研究考察了不同的方法是否能够正确地对已建立的研究领域进行分类，并在广泛的学科范围内发现新兴领域。每种方法都是基于一组度量有效性、计算成本、人工监督成本以及与现有分类框架保持一致性的需要的度量来评估的。这项工作直接回应了一些国家科学院的建议和国家科学与工程统计中心（NCSES）的报告，这些报告建议使用计算方法对科学研究领域进行分类。长期影响是在科学和工程方面的数据收集、处理和国家关键统计报告方面的改进。

The project develops and compares cutting edge methods for the classification and analysis of American scientific research and compares them to existing manually generated approaches. The project examines the strengths and weaknesses of four computational approaches: topic models, network-based partitioning methods, Wikipedia-based labeling, and an active-learning approach. In particular, the research examines whether or not the different approaches can correctly classify established research areas and discover emerging fields across a broad range of disciplines. Each approach is evaluated based on a set of metrics measuring effectiveness, computational costs, human oversight costs, and the need to retain consistency with existing classification frameworks. The work directly responds to a number of National Academies recommendations and National Center for Science and Engineering Statistics (NCSES) reports that suggest using computational approaches to classify scientific research fields. A longer-term impact is improvement in data collection, processing, and reporting of key national statistics on science and engineering.