CAREER: Knowledge Infrastructure in the Red List of Threatened Species

这个职业奖是为了支持科学史和科学社会研究的交叉点上的研究。国际保护协会提议对保护数据库进行历史和概念研究，该数据库是由国际自然保护联盟(ICUN)维护的濒危物种红色名录。RLTS被广泛誉为将科学与行动联系起来的伟大成功故事之一，IUCN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。在该项目中，RLTS被概念化为知识基础设施，它被理解为一个由人、人工制品和机构组成的强大网络，用于生成、共享和维护关于人类和自然世界的特定知识。这一研究项目将对RLTS的一般假设、优点和局限性进行第一次历史情境分析，作为知识基础设施支持数千参与者合作的全球研究的示范案例。项目成果将概括为其他旨在利用分散的信息来源产生权威科学知识的知识基础设施的设计。对RLTS方法的拟议分析将在利用历史档案和出版文件进行研究的基础上，首次描述其始于第一次世界大战后时代的历史全貌。该项目将贡献文章文本的新数据集、科学计量学分析的新方法和描述期刊论文的新元数据，这些元数据将以开放数据和软件的形式分发。该项目将分析RLTS方法如何反映随着时间的推移而产生的妥协，如何规范其统一应用，以及全球标准化在哪里阻碍了为增加当地价值而进行定制。该项目还将构成对RLTS贡献者人口统计学的第一次研究，包括可从公共信息获得的关于参与者的地理位置和就业类型的信息。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.