Franklin

Quantitatively measuring U.S. legislative district compactness using survey methods and machine learning models.

Other records:
Other Title:
Quantitatively measuring United States legislative district compactness using survey methods and machine learning models
Publication:
London : SAGE Publications Ltd, 2019.
Format/Description:
Video
1 online resource (1 video file (00:11:12)) : sound, colour
Subjects:
Quantitative research.
Legislators -- United States.
Social surveys.
Machine learning.
Language:
Closed-captions in English.
System Details:
digital
Summary:
Aaron Kaufman, PhD candidate at Harvard University's Department of Government and the Institute for Quantitative Social Science, discusses using survey methods and machine learning models to quantitatively measure U.S. legislative district compactness, including what legislative district compactness is and how it is measured, the purpose of this research, data collection and analysis, challenges faced and overcome, advice for those interested in similar research, and the impact of this research.
Participant:
Academic, Aaron Kaufman.
Notes:
Description based on XML content.
Contributor:
Kaufman, Aaron, Academic.
ISBN:
9781526499264
OCLC:
1103470902
Access Restriction:
Restricted for use by site license.
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