发表机构
University of Vermont; Rutgers University-Newark; Vermont Complex Systems Institute; University of Illinois Chicago; Complexity Science Hub, Vienna（佛蒙特大学; 罗格斯大学纽瓦克分校; 佛蒙特复杂系统研究所; 伊利诺伊大学芝加哥分校; 维也纳复杂科学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个计算流程，对爱荷华州2.1万份跨地方协议进行分类并提取财务关系，构建有向网络，揭示县为关键服务提供者、城市多为委托方，为追踪公共资金流动提供可复用方法。
AI 中文摘要
跨地方协议是地方政府正式化公共服务交付合作的主要工具之一，然而这些合同中编码的制度与财务内容在大规模系统分析中一直难以获取。本文介绍了一个端到端的计算流程，用于按制度形式对政府间协议进行分类，并提取服务合同中委托方与代理方之间的财务关系。该流程应用于爱荷华州28E档案（N=21,629），这是美国最大的跨地方协议数据集，结合了基于LLM的摘要与分类，使用LLaMA 3.1、GPT 5.2 Pro和Gemini 3 Pro进行四类分类任务，将协议区分为服务合同、资源共享协议、联合运营协议或新联合实体协议。我们还识别了这些协议和合同中的财务委托方与代理方，以及由此产生的美元金额，并将其表示在一个有向网络中。由此产生的财务网络围绕少数主导服务提供者组织，县作为结构上最多样化的参与者，而城市主要作为委托方。通过首次使爱荷华州跨地方协议的内容可大规模分析，该流程建立了一种可复用的方法论，研究人员和州机构可应用它来追踪公共资金如何在地方政府间流动，并识别高度依赖少数提供者的实体。
英文摘要
Interlocal agreements are one of the primary instruments through which local governments formalize collaboration for public service delivery, yet the institutional and financial content encoded in these contracts has remained inaccessible to systematic analysis at scale. This paper introduces an end-to-end computational pipeline for classifying intergovernmental agreements by institutional form and extracting financial relationships between principals and agents in service contracts. Applied to Iowa's 28E archive (N = 21,629), the largest dataset of interlocal agreements in the United States, the pipeline combines LLM-based summarization and classification across LLaMA 3.1, GPT 5.2 Pro, and Gemini 3 Pro on a four-class classification task that distinguishes agreements as either service contracts, resource sharing agreements, joint operations agreements, or new joint entity agreements. We also identify the financial principal and agent in these agreements and contracts, as well as the resulting dollar amounts and represent them on a directed network. The resulting financial network is organized around a small number of dominant service providers, with counties serving as the most structurally versatile actors, and cities as predominantly principals. By rendering the content of Iowa interlocal agreements analyzable at scale for the first time, this pipeline establishes a reusable methodology that researchers and state agencies can apply to track how public dollars move across local governments and to identify entities that depend heavily on a small number of providers.
Comments22 pages, 8 tables, 7 figures
