Journal article
Measuring the significance of policy outputs with positive unlabeled learning
- Abstract:
- Identifying important policy outputs has long been of interest to political scientists. In this work, we propose a novel approach to the classification of policies. Instead of obtaining and aggregating expert evaluations of significance for a finite set of policy outputs, we use experts to identify a small set of significant outputs and then employ positive unlabeled (PU) learning to search for other similar examples in a large unlabeled set. We further propose to automate the first step by harvesting ‘seed’ sets of significant outputs from web data. We oer an application of the new approach by classifying over 9,000 government regulations in the United Kingdom. The obtained estimates are successfully validated against human experts, by forecasting web citations, and with a construct validity test.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, 3.4MB, Terms of use)
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- Publisher copy:
- 10.1017/S000305542000091X
Authors
- Publisher:
- Cambridge University Press
- Journal:
- American Political Science Review More from this journal
- Volume:
- 115
- Issue:
- 1
- Pages:
- 339 - 346
- Publication date:
- 2020-10-19
- Acceptance date:
- 2020-08-31
- DOI:
- EISSN:
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1537-5943
- ISSN:
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0003-0554
- Language:
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English
- Keywords:
- Pubs id:
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1129721
- Local pid:
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pubs:1129721
- Deposit date:
-
2020-09-02
Terms of use
- Copyright holder:
- Zubek et al.
- Copyright date:
- 2020
- Rights statement:
- © The Author(s), 2020. Published by Cambridge University Press on behalf of the American Political Science Association
- Notes:
- This is the submitted version of the article. The final version will be available from Cambridge University Press at: https://doi.org/10.1017/S000305542000091X
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