Councils in Action: Automating the Curation of Municipal Governance Data for Research

Abstract

Large scale comparative research into municipal governance is often prohibitively difficult due to a lack of high‐quality data. Recent advances in speech‐to‐text algorithms and natural language processing techniques has made it possible to more easily collect and analyze this type of data. In this paper, we introduce an open‐source platform, the Council Data Project (CDP), to curate novel datasets for research into municipal governance. The contribution of this work is two‐fold: 1. We demonstrate that CDP, as an infrastructure, can be used to assemble reliable comparative data across municipalities; 2. We provide exploratory analysis to show how CDP data can be used to gain insight into how municipal governments perform over time. We conclude by describing future directions for research on and with CDP such as the development of machine learning models for speaker annotation, outline generation, and named entity recognition to the linking of data for large‐scale comparative research.

Speakerbox: Few-Shot Learning for Speaker Identification with Transformers

“…In our initial research, we first annotated ~10 hours of audio using the Gecko platform in ~12 hours of time, we then used our diarization and labeling method to annotate an additional ~21 hours of audio in ~6 hours of time. In total, the dataset was annotated and compiled in less than ~18 hours and contained ~31 hours of audio from meetings of the Seattle City Council (Eva & Weber, 2022). The model trained from the annotated dataset with the best precision and recall achieved 0.977 and 0.976 respectively.…”