Culturally diverse expert teams have yet to bring comprehensive linguistic diversity to intergovernmental ecosystem assessments

• Main Authors: LYNCH, Abigail J., ÁLVARO, Fernández-Llamazares, PALOMO, Ignacio, JAUREGUIBERRY, Pedro, TATSUYA, Amano, BASHER, Zeenatul, LIM, Michelle, MWAMPAMBA, Tuyeni Heita, SAMAKOV, Aibek, SELOMANE, Odirilwe, LIM, Michelle Mei Ling
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: cultural diversity; non-English languages; non-Anglophones; intergovernmental process; IPBES; knowledge; language; language barriers; scientific literature; representation; Environmental Law; Environmental Sciences
• Online Access: https://ink.library.smu.edu.sg/sol_research/4099; https://ink.library.smu.edu.sg/context/sol_research/article/6057/viewcontent/1_s2.0_S2590332221000488_pvoa.pdf
• Institution: Singapore Management University

Multicultural representation is a stated goal of many global scientific assessment processes. These processes aim to mobilize a broader, more diverse knowledge base and increase legitimacy and inclusiveness of these assessment processes. Often, enhancing cultural diversity is encouraged through involvement of diverse expert teams and sources of knowledge in different languages. In this article, we examine linguistic diversity, as one representation of cultural diversity, in the eight published assessments of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). Our results show that the IPBES assessment outputs are disproportionately filtered through English-language literature and authors from Anglophone countries. To incorporate more linguistic diversity into global ecosystem assessment processes, we present actionable steps for global science teams to recognize and incorporate non-English-language literature and contributions from non-Anglophones. Our findings highlight the need for broad-scale actions that enhance inclusivity in knowledge synthesis processes through balanced representation of different knowledge holders and sources.