Replication Data for: Policy and the Structure of Roll Call Voting in the U.S. House
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Competition in the U.S. Congress has been characterized along a single, left-right ideological dimension. We challenge this characterization by showing that the content of legislation has far more predictive power than alternative measures, most notably legislators’ ideological positions as derived from scaling roll call votes. Using a machine learning approach, we identify a topic model for final passage votes in the 111th through the 113th House of Representatives and conduct out-of-sample tests to evaluate the predictive power of bill topics relative to other measures. We find that bill topics and congressional committees are important for predicting roll call votes but that other variables, including member ideology, lack predictive power. These findings raise serious doubts about the claim that congressional politics can be boiled ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["machine learning, Congress"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Competition in the U.S. Congress has been characterized along a single, left-right ideological dimension. We challenge this characterization by showing that the content of legislation has far more predictive power than alternative measures, most notably legislators’ ideological positions as derived from scaling roll call votes. Using a machine learning approach, we identify a topic model for final passage votes in the 111th through the 113th House of Representatives and conduct out-of-sample tests to evaluate the predictive power of bill topics relative to other measures. We find that bill topics and congressional committees are important for predicting roll call votes but that other variables, including member ideology, lack predictive power. These findings raise serious doubts about the claim that congressional politics can be boiled ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Competition in the U.S. Congress has been characterized along a single, left-right ideological dimension. We challenge this characterization by showing that the content of legislation has far more predictive power than alternative measures, most notably legislators’ ideological positions as derived from scaling roll call votes. Using a machine learning approach, we identify a topic model for final passage votes in the 111th through the 113th House of Representatives and conduct out-of-sample tests to evaluate the predictive power of bill topics relative to other measures. We find that bill topics and congressional committees are important for predicting roll call votes but that other variables, including member ideology, lack predictive power. These findings raise serious doubts about the claim that congressional politics can be boiled 
machine learning, Congress