ICAR Research Data Repository for Knowledge Management
Replication Data for: Measuring the impact of campaign finance on congressional voting: A machine learning approach
Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)
View Archive Info| Field | Value | |
| Title |
Replication Data for: Measuring the impact of campaign finance on congressional voting: A machine learning approach
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| Identifier |
https://doi.org/10.7910/DVN/DHQQHX
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| Creator |
Lalisse, Matthias
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| Publisher |
Harvard Dataverse
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| Description |
Replication data for the paper: "Measuring the impact of campaign finance on congressional voting: A machine learning approach" Includes: * metadata for legislators and bills, * text embeddings for legislative summaries (sourced from ProPublica Congress Database). Includes 768d LongFormer embeddings and 2d embeddings for visualization (UMAP and Isomap), * legislator embeddings: 100d PCA on legislators' financial disclosures, as well as 2d visualization embeddings (UMAP and Isomap), * scripts for running the classification and RSA analyses. Up to 100d embeddings are provided from the output of PCA for both bills and legislators. See README.ipynb for a tour of the datasets as well as starter code. |
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| Subject |
Computer and Information Science
Social Sciences |
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| Contributor |
Lalisse, Matthias
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