The mission of StateLegiscraper is to make accessible text corpora of political, social, and scholarly significance that can build greater public transparency and academic knowledge about public policymaking and state-level politics.

The motivation to create StateLegiscraper emerged from my dissertation's data collection efforts, and my desire to provide general users access to previously difficult to obtain state legislature data. I am the main author of the StateLegiscraper package, where I actively develop and code the scrape and process functions for all available state coverage. I am continually building documentation on the repoistory, including a detailed installation guide, package design specifications, and user guides to implement the package. The following Jupyter notebook example provides documentation on how to use StateLegiscraper, with sample code and data outputs.

Abstract: In 1971, the first U.S. school finance court case was decided in California's Serrano v. Priest that aimed to reform a state's education funding structure. And despite widespread school finance reform efforts since, racial and economic inequities in education have persisted across the nation. How is it that fifty years of education funding reform activity have not yielded substantive change that bends the education system towards resource justice? My dissertation examines the politics of school finance reform and the manner that diverse policy actors in state legislatures negotiate policy solutions in the aftermath of major education funding litigation. To contextualize trends of existing racial funding disparities, I build an original theoretical framework that connects the politics of public education resource allocation to racialized political geographies and racial capitalism. I illustrate how racial capitalist logics may emerge during the school finance reform policy process as state legislators and policy advocates debate potential solutions, and how coded, racialized political discourse may map onto policy outputs. I employ natural language processing methods and social network analysis to conduct this study at scale and across time to capture the entirety of a state legislature's response to school funding court mandates. I summarize the totality of policy solutions presented by state legislators and policy advocates – creating a complete account of all publicly discussed school finance reform solutions. My dissertation will assist policymakers and policy advocates to better understand the political realities behind school finance reform solutions, and describe practiced strategies that may advance racial equity and resource justice in education.

The Spatial Politics of Public Education and Public Housing

This Shiny interactive utilizes a multi-dimensional dataset to explore the relationship between public education and public housing. The project's broader research question asks, what types of schooling environments do students living in public housing experience? For this particular interactive, users explore the spatial distribution of student racial groups across Seattle and the relationship of Seattle Public Schools' 2018 budget category amounts against their student racial group percentage. HOLC redlining map boundaries and descriptions from 1938 are provided to examine the potential influence of historical racial segregationist policies on current spatial organization of educational opportunities by race.

Washington State School District Budgets and Students of Color

This Shiny interactive explores Washington state school district budgets and their relationship to the district's percentage share of students of color from 1993-2016. The work is part of a broader research project that investigates: is there a relationship between race and school funding in Washington state, specifically the distribution of state equalization funding to districts as a result of policy reform? The purpose of this specific interactive is to serve as an exploratory data analysis that will build towards refining parametric specification and model construction. The main dataset used is from Bruce Baker's School Finance Indicators Dataset: District Indicators (Baker et al., 2019). The unit of analysis within the complete dataset is for all school districts across the nation from 1987-2016 and this study utilizes a subset of all Washington state observations between the years 1993-2016. There are two visualizations of interest to this interactive: a time-series plot of district budgets from 1993-2016 and a summary statistics table with a more granular breakdown of mean, median, range, and n. Users can select the specific revenue source to visualize in the time-series plot and select a specific year in the summary statistics tables. The time-series plot includes a 95% confidence interval of the population mean calculated by a nonparametric bootstrap without assuming normality.