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Measuring the Significance of Policy Outputs with Positive Unlabeled Learning

  • RADOSLAW ZUBEK (a1), ABHISHEK DASGUPTA (a1) and DAVID DOYLE (a1)

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 offer 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.

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Corresponding author

Radoslaw Zubek, Associate Professor, Department of Politics and International Relations, University of Oxford, radoslaw.zubek@politics.ox.ac.uk.
Abhishek Dasgupta, Research Software Engineer, Department of Computer Science, University of Oxford, abhishek.dasgupta@cs.ox.ac.uk.
David Doyle Associate Professor, Department of Politics and International Relations, University of Oxford, david.doyle@politics.ox.ac.uk.

Footnotes

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We thank Ken Benoit, Shaun Bevan, Jack Blumenau, Ruth Dixon, Andy Eggers, Mikolaj Firlej, Diego Garzia, Felix Krawatzek, Ben Lauderdale, Innar Liiv, Marcin Matczak, Nolan McCarty, Sharon Meraz, Scot Peterson, Ulrich Sieberer, Jayne Sunley, Mariusz Zubek, and three anonymous reviewers for helpful comments and suggestions. We acknowledge the financial support from BA/Leverhulme Small Research Grant (SG152293) and John Fell OUP Research Fund (151/025). Abhishek Dasgupta acknowledges support from the Centre for Technology and Global Affairs. David Doyle acknowledges support from the Jackie Lambert Research Fund at St Hugh’s College. Our thanks to Kwok Cheung, Tom Fleming, Vladimir Mikulik, Paul Moore, Edward Percarpio, Hugh Thomas, Zoe Harrison, Neerja Gurnani, Atrayee De, and Jacob Yu for excellent research assistance. Replication files are available at the American Political Science Review Dataverse: https://doi.org/10.7910/DVN/1XXDMW.

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References

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Measuring the Significance of Policy Outputs with Positive Unlabeled Learning

  • RADOSLAW ZUBEK (a1), ABHISHEK DASGUPTA (a1) and DAVID DOYLE (a1)

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