Short abstract

A machine learning pipeline that detects policy-relevant science and enables the first systematic analysis of the science-policy interface at scale. This talk reveals key insights in how scientific evidence flows into policymaking, with implications for improving evidence-based governance.

Long abstract

The gap between scientific evidence and policymaking remains a critical challenge for effective governance. Current research on the science-policy interface has largely relied on analysing how governments cite scientific papers, overlooking potentially valuable research that fails to reach policymakers.

We present a novel machine learning pipeline that detects policy-relevant research via paper abstracts. This enables the first systematic, large-scale analysis of both used and overlooked policy-relevant science. When applied to large bibliometric databases, our model reveals previously hidden patterns in how scientific evidence flows into policymaking, including institutional factors and author characteristics that influence policy uptake. These insights inspire new tools for improving the transmission of scientific evidence into policy, ultimately supporting more effective evidence-based governance.