CAREER: Knowledge Infrastructure in the Red List of Threatened Species

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.