Making Local Government Contracts Legible: A Computational Pipeline for Classifying and Mapping Intergovernmental Service Agreements

Research details a new computational pipeline designed to classify intergovernmental service agreements and extract critical institutional and financial data at scale. Applied to a substantial dataset of local government contracts in Iowa, this method leverages advanced Large Language Models (LLMs) to overcome prior challenges in systematically analyzing such agreements, which are fundamental to public service delivery.

This development is strategically important as it addresses a long-standing challenge in understanding intergovernmental collaboration and resource allocation. By enabling systematic analysis of service agreements, it provides unprecedented insights into the operational and financial mechanics of public service delivery across jurisdictions, fostering improved governance and efficiency.

Interlocal agreements, crucial for formalizing public service collaboration, have historically been difficult to analyze systematically due to their inaccessible institutional and financial content.

Forward consideration, not a verified fact.