Sietsma, A.J., Ford, J.D. orcid.org/0000-0002-2066-3456 and Minx, J.C. (2023) The next generation of machine learning for tracking adaptation texts. Nature Climate Change, 14. pp. 31-39. ISSN 1758-678X
Abstract
Machine learning presents opportunities for tracking evidence on climate change adaptation, including text-based methods from natural language processing. In theory, such tools can analyse more data in less time, using fewer resources and with less risk of bias. However, the first generation of adaptation studies have delivered only proof of concepts. Reviewing these first studies, we argue that future efforts should focus on creating more diverse datasets, investigating concrete hypotheses, fostering collaboration and promoting ‘machine learning literacy’, including understanding bias. More fundamentally, machine learning enables a paradigmatic shift towards automating repetitive tasks and makes interactive ‘living evidence’ platforms possible. Broadly, the adaptation community is failing to prepare for this shift. Flagship projects of organizations such as the IPCC could help to lead the way.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Limited 2023. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s41558-023-01890-3 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Sustainability Research Institute (SRI) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Jan 2024 09:49 |
Last Modified: | 27 Jun 2024 00:13 |
Status: | Published |
Publisher: | Nature Research |
Identification Number: | 10.1038/s41558-023-01890-3 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208463 |