Tracing Influence & Predicting Impact in Science

追踪影响力

• 批准号: 1158803
• 负责人: James Evans
• 金额: $ 50.69万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1158803&HistoricalAwards=false
• 关键词: Tracing; Influence; & Predicting; Impact

This project develops powerful new measures and models that assess and predict the impact of academic research on science, technology, and the public. The evaluation of research has historically relied on quantity as a proxy for quality: progress is inferred from the amount of research produced and attention garnered, the number of articles is tallied, and academic citations and media mentions are summed. These quantities are cheaply measured, but cannot capture how fundamental a contribution is or identify the nature of its impact. Such nuanced evaluation of research quality demands that new claims be considered in the context of previous work. Building on progress in the measurement of scientific and technological novelty, as well as phylogenetic models of evolution, advances in computational language understanding, and the increasing electronic availability of historical and contemporary scientific texts, this project creates computational tools to identify scientific claims, embed them in their historical, conceptual, and geographical context, and thereby provide a multidimensional evaluation of the nature and scope of their impact. Claims extracted from text are complemented by article citations and unpublished expert opinions about paths of scientific influence, elicited through newly developed interactive online games. Taken together, these data provide the input for probabilistic models that exploit patterns in the structure of scientific language, the graph of citations, and expert opinion to assess the evolutionary and ecological importance of particular concepts and claims, articles and journals, scientists and institutions within the unfolding network of scientific and technical innovation. These rich models provide the nuanced assessment of research quality and impact needed by scientists and policy makers at all levels, from individual scientists choosing a new research focus to departments making tenure decisions or federal agencies setting funding priorities. The methods and models developed by this project are relevant to scientists and policy makers in many areas. The initial focus, however, is on the broad field of chemistry and its diverse, overlapping subfields, from analytical, physical and organic chemistry to environmental and agricultural chemistry, pharmaceuticals and toxicology. Broader ImpactsThis project enables the creation of high resolution, dynamic maps of scientific influence in several broadly related fields. In education, such maps can be made interactive and serve as a teaching tool to help students understand the contributions and forces that have shaped their field of science. These maps also facilitate the social analysis of scientific production with a new level of precision, while orienting researchers and policy makers to the forces that shape scientific importance and technological promise. Most importantly, these influence maps provide the foundation for a collection of sharp measures that can identify different types of importance and the benefits and risks associated with each so that they can be assessed and balanced by researchers and policy-makers. By clarifying what is published and where, and by tracing the conceptual careers of scientists and inventors, this research offers insight into the factors that guide and deflect scientific attention and how these factors can be harnessed to achieve the greatest impact from public investments in science and technology.

该项目开发了强大的新措施和模型，用于评估和预测学术研究对科学、技术和公众的影响。历史上，对研究的评估一直依赖于数量作为质量的代表：进展是根据研究成果和获得的关注的数量来推断的，文章的数量是统计的，学术引用和媒体提及的次数是汇总的。这些数量是廉价衡量的，但不能反映贡献的根本程度，也不能确定其影响的性质。对研究质量的这种细微差别的评估要求在以前工作的背景下考虑新的主张。在衡量科学和技术新颖性方面的进展，以及进化的系统发生模型、计算机语言理解方面的进展以及历史和当代科学文本的电子可获得性日益增加的基础上，该项目创建了计算工具，以确定科学主张，将其嵌入其历史、概念和地理背景中，从而对其影响的性质和范围进行多方面的评估。从文本中提取的主张得到了文章引用和未发表的关于科学影响路径的专家意见的补充，这些都是通过新开发的互动在线游戏引起的。综上所述，这些数据为概率模型提供了信息，这些模型利用科学语言结构、引文图表和专家意见的模式，评估正在展开的科技创新网络中特定概念和主张、文章和期刊、科学家和机构的进化和生态重要性。这些丰富的模型提供了对各级科学家和政策制定者所需的研究质量和影响的细微差别评估，从个别科学家选择新的研究重点，到制定终身教职决定的部门或联邦机构设定资金优先顺序。该项目开发的方法和模型与许多领域的科学家和政策制定者相关。然而，最初的重点是广泛的化学领域及其不同的、重叠的子领域，从分析、物理和有机化学到环境和农业化学、制药和毒理学。更广泛的影响该项目能够在几个广泛相关的领域创建高分辨率、动态的科学影响力地图。在教育中，这样的地图可以成为互动的，并作为一种教学工具，帮助学生了解塑造他们科学领域的贡献和力量。这些地图还有助于以新的精度对科学成果进行社会分析，同时将研究人员和政策制定者引向塑造科学重要性和技术前景的力量。最重要的是，这些影响图为一系列尖锐措施提供了基础，这些措施可以确定不同类型的重要性以及与每种措施相关的好处和风险，以便研究人员和政策制定者能够评估和平衡这些措施。通过澄清发表了什么以及在哪里发表，并通过追踪科学家和发明家的概念职业生涯，这项研究提供了对引导和转移科学注意力的因素的洞察，以及如何利用这些因素来实现公共科技投资的最大影响。