Overton Engage: A Structured Database and Matching System for Academic Policy Engagement Opportunities

TL;DR

Overton Engage is a publicly accessible database and semantic matching system that links academic researchers to policy engagement opportunities, aiming to improve discovery and participation in policy-related research activities.

Contribution

This work introduces a structured database and matching system for academic policy engagement opportunities, addressing fragmentation and discoverability issues.

Findings

Opportunities are unevenly distributed across domains and countries.

Higher publication volume correlates with better match rates.

Research specialization can offset lower publication volume.

Abstract

Academic policy engagement, the structured processes through which researchers contribute evidence and expertise to public decision-making, is shaped not only by research quality but by the accessibility of engagement opportunities. In practice, these opportunities are fragmented across institutions and platforms, unevenly advertised, and difficult to discover systematically (Parker et al., 2022), limiting both individual participation and comparison. We present Overton Engage (https://app.overton.io/ui/opportunities), a structured database of publicly documented academic policy engagement opportunities, together with a semantic matching system that links opportunities to researchers based on similarity between opportunity descriptions and publication records. We characterise the composition of the database across policy domains, countries, and opportunity types, and present UK-focused analyses comparing engagement opportunity topics with published policy documents. We further demonstrate an illustrative comparison of consultation topics between the UK and Australia, and apply a matching system to assess how closely research produced by UK higher education institutions aligns, topically, with domestic policy opportunities. Our results suggest that publicly documented engagement opportunities are unevenly distributed across policy domains and countries, though this may reflect collection bias. Matching analyses reveal a positive relationship between institutional publication volume and high-confidence match rates, but also that research specialisation can compensate for lower output volume in specific policy domains. The database itself is freely available and we welcome collaboration from researchers, policymakers, and institutions.