CAREER: Knowledge Infrastructure in the Red List of Threatened Species

该职业奖旨在支持科学史和科学社会研究交叉领域的研究。PI建议对由国际自然保护联盟（ICUN）维护的濒危物种红色名录（RLTS）保护数据库进行历史和概念研究。RLTS被广泛誉为将科学与行动联系起来的伟大成功案例之一，世界自然保护联盟已成为综合科学知识为保护政策和决策提供信息的最大和最有影响力的国际组织之一。RLTS在项目中被概念化为知识基础设施，它被理解为生成、共享和维护关于人类和自然世界的特定知识的人、工件和机构的健壮网络。该研究项目将首次对RLTS的一般假设、优点和局限性进行历史分析，并将其作为支持数千参与者进行全球合作研究的知识基础设施的典型案例。项目结果将概括为其他知识基础设施的设计提供参考，这些基础设施旨在利用分散的信息来源产生权威的科学知识。拟议的RLTS方法分析将是第一个根据使用历史档案和出版文件的研究，描述其从第一次世界大战后开始的整个历史的分析。该项目将提供文章文本的新数据集，科学计量分析的新方法，以及描述期刊文章的新元数据，这些将作为开放数据和软件分发。该项目将分析RLTS方法如何反映随时间累积的妥协，如何规范其统一应用，以及全球标准化在哪些方面抑制了为增加本地价值而进行的定制。该项目还将构成对RLTS贡献者人口统计数据的首次研究，包括从公共信息中获得的有关参与者的地理位置和就业类型的信息。该奖项反映了美国国家科学基金会的法定使命，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。

This CAREER award is to support research at the intersection of history of science and social studies of science. The PI proposes to engage in an historical and conceptual study of a conservation database, the Red List of Threatened Species (RLTS), which is maintained by the International Union for the Conservation of Nature (ICUN). The RLTS is widely hailed as one of the great success stories in connecting science to action, and the IUCN has become one of the largest and most influential international organizations synthesizing scientific knowledge to inform conservation policy and decision making. RLTS is conceptualized in the project as knowledge infrastructure, which is understood as a robust networks of people, artifacts, and institutions that generate, share, and maintain specific knowledge about the human and natural worlds. This research project will develop the first historically-situated analysis of the RLTS’s general assumptions, virtues, and limitations as an exemplary case of knowledge infrastructure supporting cooperative global research among thousands of participants. Project results will generalize to inform the design of other knowledge infrastructures that aim to produce authoritative scientific knowledge using decentralized sources of information.The proposed analysis of the RLTS methodology will be the first to characterize the full arc of its history starting in the post-World War I era based on research using historical archives and published documents. The project will contribute novel datasets of article text, novel methodologies for scientometric analysis, and novel metadata describing journal articles, which will be distributed as open data and software. The project will analyze how the RLTS methodology reflects compromises accrued over time, how its uniform application is regulated, and where global standardization inhibits customization for increased local value. The project will also constitute the first study of RLTS contributor demographics, including information about geographic location and employment type of participants where available from public information.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.