{
  "slug": "using-deep-and-active-learning-classifiers-to-identify-congressional-delegation-to-administrative-agencies",
  "title": "Using Deep and Active Learning Classifiers to Identify Congressional Delegation to Administrative Agencies",
  "authors": "with Gregory Spell",
  "venue": null,
  "year": null,
  "category": "institutions",
  "section": "workingPapers",
  "status": "working-paper",
  "status_label": "Working paper",
  "visibility": "public-pdf",
  "doi": null,
  "canonical_url": null,
  "citation_authors": [
    "Lerner, Joshua Y.",
    "Spell, Gregory"
  ],
  "citation_publication_date": "2020/07/29",
  "summary": "Congressional oversight of the federal bureaucracy remains key to understanding implementation of major laws. This paper uses text-as-data methods to classify bill sections by their role in delegating authority to administrative agencies. It introduces an active learning approach to text classification, applying an iteratively improving coding scheme that enhances existing supervised learning approaches and supports systematic study of the statutory scope of administrative agencies.",
  "metadata_url": "https://jyl19.github.io/papers/using-deep-and-active-learning-classifiers-to-identify-congressional-delegation-to-administrative-agencies/metadata.json",
  "summary_url": "https://jyl19.github.io/papers/using-deep-and-active-learning-classifiers-to-identify-congressional-delegation-to-administrative-agencies/summary.md",
  "pdf_url": "https://jyl19.github.io/papers/using-deep-and-active-learning-classifiers-to-identify-congressional-delegation-to-administrative-agencies/paper.pdf"
}