ArticlePatterns (New York, N.Y.)2025
Uncovering drivers of climate research in policy with pretrained language models.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- COVID-19 education policy often relied on older research despite available new evidence.STAR protocols · 2026Article
- Structure of scientific knowledge flows to intergovernmental organizations.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Uncovering drivers of climate research in policy with pretrained language models.Patterns (New York, N.Y.) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Evidence-based policymaking is crucial for addressing societal challenges, yet factors driving research uptake in policy remain unclear. Previous studies have not accounted for the confounding effect of policy relevance, potentially skewing conclusions about impact drivers. Using climate change as a case study, we employ pretrained language models to identify semantically similar research paper pairs where one is cited in policy and the other is not, controlling for inherent policy relevance. This approach allows us to isolate the effects of various factors on policy citation likelihood. We find that in climate change, academic citations are the strongest predictor of policy impact, followed by media mentions. This computational method can be extended to other variables as well as different scientific domains to enable comparative analysis of policy uptake mechanisms across fields.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.