The following work seeks to examine the relationship between special interests, political parties, and major donors who help to fund certain types of interest group coalitions. Specifically, the work will seek to further understand the relationship between the American Legislative Exchange Council (ALEC), the groups funders, their ideology and relationship to political parties, and the impact these factors have on policy at the state level. Using a sample of 171 model bills drafted by ALEC, we utilize preliminary natural language processing methods to identify key topics existant in model bills and compare those to a sample of all legislation passed in the state of California between 1989 and 1991. We find preliminary results that suggest further application of supervised machine learning to begin to identify language in model legislation that appears in state legislatures. The proposed methods can begin to help scholars further expand on the relationship between donors, political parties, and the larger policy diffusion network that helps to ensure model legislation passes for the benefit of the coalition that seeks its implementation.
","abstract_html":"<p>The following work seeks to examine the relationship between special interests, political parties, and major donors who help to fund certain types of interest group coalitions. Specifically, the work will seek to further understand the relationship between the American Legislative Exchange Council (ALEC), the groups funders, their ideology and relationship to political parties, and the impact these factors have on policy at the state level. Using a sample of 171 model bills drafted by ALEC, we utilize preliminary natural language processing methods to identify key topics existant in model bills and compare those to a sample of all legislation passed in the state of California between 1989 and 1991. We find preliminary results that suggest further application of supervised machine learning to begin to identify language in model legislation that appears in state legislatures. The proposed methods can begin to help scholars further expand on the relationship between donors, political parties, and the larger policy diffusion network that helps to ensure model legislation passes for the benefit of the coalition that seeks its implementation.</p>","abstract_has_math":false,"creators":["Ulloa, Jordan James"],"institution":null,"degree_name":"Politics, MA","degree_level":"Open Access Master's Thesis","degree_discipline":"School of Social Science, Politics, and Evaluation","degree_department":null,"school":null,"contributors":["Christopher Krewson"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-01-01T08:00:00Z","date_published":"2020-01-01T08:00:00Z","updated_at":"2026-07-24T01:39:59Z","subjects":["ALEC","Model legislation","NLP","Special interests","State policy"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/425","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Christopher Krewson"]},{"key":"dc:creator","label":"Author","values":["Ulloa, Jordan James"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-03-23T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["School of Social Science, Politics, and Evaluation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Master's Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Politics, MA"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ALEC","Model legislation","NLP","Special interests","State policy"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/425"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The following work seeks to examine the relationship between special interests, political parties, and major donors who help to fund certain types of interest group coalitions. Specifically, the work will seek to further understand the relationship between the American Legislative Exchange Council (ALEC), the groups funders, their ideology and relationship to political parties, and the impact these factors have on policy at the state level. Using a sample of 171 model bills drafted by ALEC, we utilize preliminary natural language processing methods to identify key topics existant in model bills and compare those to a sample of all legislation passed in the state of California between 1989 and 1991. We find preliminary results that suggest further application of supervised machine learning to begin to identify language in model legislation that appears in state legislatures. The proposed methods can begin to help scholars further expand on the relationship between donors, political parties, and the larger policy diffusion network that helps to ensure model legislation passes for the benefit of the coalition that seeks its implementation.
"]},{"key":"dc:title","label":"Title","values":["Model Legislation as a Measure of Special Interest Influence: The American Legislative Exchange Council and the Koch Network"]}]}],"canonical_facts":{"dc:contributor":["Christopher Krewson"],"dc:creator":["Ulloa, Jordan James"],"dc:date.available":["2023-03-23T07:00:00Z"],"dc:description.abstract":["The following work seeks to examine the relationship between special interests, political parties, and major donors who help to fund certain types of interest group coalitions. Specifically, the work will seek to further understand the relationship between the American Legislative Exchange Council (ALEC), the groups funders, their ideology and relationship to political parties, and the impact these factors have on policy at the state level. Using a sample of 171 model bills drafted by ALEC, we utilize preliminary natural language processing methods to identify key topics existant in model bills and compare those to a sample of all legislation passed in the state of California between 1989 and 1991. We find preliminary results that suggest further application of supervised machine learning to begin to identify language in model legislation that appears in state legislatures. The proposed methods can begin to help scholars further expand on the relationship between donors, political parties, and the larger policy diffusion network that helps to ensure model legislation passes for the benefit of the coalition that seeks its implementation.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/425"],"dc:subject":["ALEC","Model legislation","NLP","Special interests","State policy"],"dc:title":["Model Legislation as a Measure of Special Interest Influence: The American Legislative Exchange Council and the Koch Network"],"thesis:degree_discipline":["School of Social Science, Politics, and Evaluation"],"thesis:degree_level":["Open Access Master's Thesis"],"thesis:degree_name":["Politics, MA"]},"updated_at":"2026-07-24T01:39:59Z"}