Measuring the Significance of Policy Outputs with Positive Unlabeled Learning
Bibliographic Data
| ID | 3494558 |
|---|---|
| Authors | Radoslaw Zubek (0000-0003-1006-9100, University of Oxford, corresponding author), Abhishek Dasgupta (0000-0003-4420-0656, University of Oxford, corresponding author), David Doyle (0000-0002-0157-0115, University of Oxford, corresponding author) |
| Year | 2021 |
| Volume | 115 |
| Issue | 1 |
| Pages | 339-346 |
| Publication date | 2021-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Political Science Review (JOURNAL) |
| Journal identifiers | ISSN: 0003-0554 • E-ISSN: 1537-5943 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/s000305542000091x |
| OpenAlex | W3094257370 |
| Language | EN |
| Citations received | 3 |
| References cited | 13 |
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
Construct (python library · Data mining · Government (linguistics · Machine learning · Policy learning · Set (abstract data type · Test (biology · Test set · Training set · Artificial Intelligence · Computer Science · Electoral Systems and Political Participation · Judicial and Constitutional Studies · Political Influence and Corporate Strategies
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| Unique citing works | 3 |
|---|---|
| Citations per year | 1 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 3 |