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The next generation of machine learning for tracking adaptation texts
Resource type
Journal Article
Authors/contributors
- Sietsma, Anne J. (Author)
- Ford, James D. (Author)
- Minx, Jan C. (Author)
Title
The next generation of machine learning for tracking adaptation texts
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.
Publication
Nature Climate Change
Volume
14
Issue
1
Pages
31-39
Date
2023-12-27
Journal Abbr
Nat. Clim. Chang.
Language
en
ISSN
1758-678X
Call Number
openalex: W4390273618
Rights
2023 Springer Nature Limited
Extra
openalex: W4390273618
Citation
Sietsma, A. J., Ford, J. D., & Minx, J. C. (2023). The next generation of machine learning for tracking adaptation texts. Nature Climate Change, 14(1), 31–39. https://doi.org/10.1038/s41558-023-01890-3
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