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Both datasets rely on the textual information reported in peer-reviewed articles and derived using our algorithm Gil-Clavel & Filatova (2024). The coding book for adaptation factors per actor group is provided in Gil-Clavel et al. (2024), and the coding book for classifying CCA measures as transformational adaptation is described in Gil-Clavel et al. (2023). We go beyond the traditional automatic analysis of textual data focused on the word count and topic modeling. Instead, we strive to elicit meaningful associations between the type of adaptation and factors reported to influence those decisions. We do that based on the detailed code-books designed with domain experts and on the computational Natural Language Processing algorithms that elicit reported relationships and classify them as positive, negative, or neutral.

\n\n

Related work:

\n

Gil-Clavel, Sofia, and Tatiana Filatova. “Using Natural Language Processing and Networks to Automate Structured Literature Reviews: An Application to Farmers Climate Change Adaptation.” arXiv, July 3, 2024. http://arxiv.org/abs/2306.09737.

\n

Gil-Clavel, Sofia, Thorid Wagenblast, Joos Akkerman, and Tatiana Filatova. “Patterns in Reported Adaptation Constraints: Insights from Peer-Reviewed Literature on Flood and Sea-Level Rise,” April 26, 2024. https://doi.org/10.31235/osf.io/3cqvn

\n

Gil-Clavel, Sofia, Thorid Wagenblast, and Tatiana Filatova. “Incremental and Transformational Climate Change Adaptation Factors in Agriculture Worldwide: A Natural Language Processing Comparative Analysis,” November 24, 2023. https://doi.org/10.31235/osf.io/3dp5e

\n

How to cite the datasets: Gil-Clavel, Sofia; Filatova, Tatiana, 2024, “Interrelated Climate Change Adaptation Measures and Factors”, https://doi.org/10.17026/SS/PYZCXK, DANS Data Station Social Sciences and Humanities

\n\n

Funding This work was supported by the Netherlands Organization for Scientific Research NWO VIDI grant number 191015.

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