# Using Deep and Active Learning Classifiers to Identify Congressional Delegation to Administrative Agencies

Status: working-paper
Visibility: public-pdf
Status label: Working paper
Authors: with Gregory Spell
Version: 2020/07/29

## Summary

Congressional oversight of the federal bureaucracy remains key to understanding implementation of major laws. Essential to this are theories of how and why Congress delegates powers to administrative agencies, which posit a complex relationship between individual members of Congress and the agencies they oversee.

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.

## Public Files

- Metadata: https://jyl19.github.io/papers/using-deep-and-active-learning-classifiers-to-identify-congressional-delegation-to-administrative-agencies/metadata.json
- PDF: https://jyl19.github.io/papers/using-deep-and-active-learning-classifiers-to-identify-congressional-delegation-to-administrative-agencies/paper.pdf