- Perspective
- Published:
Intersectional analysis for science and technology
- Mathias Wullum Nielsen ORCID:orcid.org/0000-0001-8759-71501,
- Elena Gissi ORCID:orcid.org/0000-0002-1666-87722,3,
- Shirin Heidari4,
- Richard Horton5,
- Kari C. Nadeau ORCID:orcid.org/0000-0002-2146-29556,7,
- Dorothy Ngila8,
- Safiya Umoja Noble9,10,11,12,13,
- Hee Young Paik14,
- Girmaw Abebe Tadesse15,
- Eddy Y. Zeng16,
- James Zou17,18,19 &
- …
- Londa Schiebinger ORCID:orcid.org/0000-0003-3438-308120,21
Naturevolume 640, pages329–337 (2025)Cite this article
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Abstract
Intersectionality describes interdependent systems of inequality related to sex, gender, race, age, class and other socio-political dimensions. By focusing on the compounded effects of social categories, intersectional analysis can enhance the accuracy and experimental efficiency of science. Here we extend intersectional approaches that were predominantly developed in the humanities, social sciences and public health to the fields of natural science and technology, where this type of analysis is less established. Informed by diverse global and disciplinary examples—from enhancing facial recognition for diverse user bases to mitigating the disproportionate impact of climate change on marginalized populations—we extract methods to demonstrate how quantitative intersectional analysis functions throughout the research process, from strategic considerations for establishing research priorities to formulating research questions, collecting and analysing data and interpreting results. Our goal is to offer a set of guidelines for researchers, peer-reviewed journals and funding agencies that facilitate systematic integration of intersectional analysis into relevant domains of science and technology. Precision in research best guides effective social and environmental policy aimed at achieving global equity and sustainability.
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Authors and Affiliations
Department of Sociology, University of Copenhagen, Copenhagen, Denmark
Mathias Wullum Nielsen
National Research Council, Institute of Marine Sciences, Venice, Italy
Elena Gissi
National Biodiversity Future Center, Palermo, Italy
Elena Gissi
GENDRO, Gender Centre, Geneva Graduate Institute, Geneva, Switzerland
Shirin Heidari
The Lancet, London, UK
Richard Horton
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
Kari C. Nadeau
Center for Climate, Health and the Global Environment, Harvard University, Boston, MA, USA
Kari C. Nadeau
Knowledge and Institutional Networks, National Research Foundation of South Africa, Pretoria, South Africa
Dorothy Ngila
Division of Social Sciences, University of California, Los Angeles, Los Angeles, CA, USA
Safiya Umoja Noble
Minderoo Initiative on Tech and Power, Center on Race and Digital Justice, University of California, Los Angeles, Los Angeles, CA, USA
Safiya Umoja Noble
Department of Gender Studies, University of California, Los Angeles, Los Angeles, CA, USA
Safiya Umoja Noble
Department of African American Studies, University of California, Los Angeles, Los Angeles, CA, USA
Safiya Umoja Noble
Department of Information Studies, University of California, Los Angeles, Los Angeles, CA, USA
Safiya Umoja Noble
Department of Foods and Nutrition, Seoul National University, Seoul, Republic of Korea
Hee Young Paik
Microsoft AI for Good Lab, Nairobi, Kenya
Girmaw Abebe Tadesse
School of Environment and Energy, South China University of Technology, Environmental Pollution, Guangzhou, China
Eddy Y. Zeng
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
James Zou
Department of Computer Science, Stanford University, Stanford, CA, USA
James Zou
Department of Electrical Engineering, Stanford University, Stanford, CA, USA
James Zou
History of Science, Stanford University, Stanford, CA, USA
Londa Schiebinger
Gendered Innovations in Science, Health and Medicine, Engineering and Environment, Stanford University, Stanford, CA, USA
Londa Schiebinger
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Contributions
M.W.N. and L.S. conceptualized, wrote and edited the article with input from all authors. E.G. contributed sections on environmental analysis. G.A.T. and J.Z. contributed sections on AI and machine learning. R.H., S.H. and D.N. reviewed and edited proposed peer-reviewed journal and funding agency guidelines. K.C.N., H.Y.P., E.Y.Z., S.U.N. and G.A.T. contributed globally relevant perspectives and materials. All authors reviewed multiple versions of the manuscript.
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Correspondence toLonda Schiebinger.
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Guidelines for reporting intersectional analysis in science and technology (GRIST).
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Nielsen, M.W., Gissi, E., Heidari, S.et al. Intersectional analysis for science and technology.Nature640, 329–337 (2025). https://doi.org/10.1038/s41586-025-08774-w
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