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Towards understanding policy design through text-as-data approaches: The policy design annotations (POLIANNA) dataset

  • Despite the importance of ambitious policy action for addressing climate change, large and systematic assessments of public policies and their design are lacking as analysing text manually is labour-intensive and costly. POLIANNA is a dataset of policy texts from the European Union (EU) that are annotated based on theoretical concepts of policy design, which can be used to develop supervised machine learning approaches for scaling policy analysis. The dataset consists of 20,577 annotated spans, drawn from 18 EU climate change mitigation and renewable energy policies. We developed a novel coding scheme translating existing taxonomies of policy design elements to a method for annotating text spans that consist of one or several words. Here, we provide the coding scheme, a description of the annotated corpus, and an analysis of inter-annotator agreement, and discuss potential applications. As understanding policy texts is still difficult for current text-processing algorithms, we envision this database to be used for building tools that help with manual coding of policy texts by automatically proposing paragraphs containing relevant information.
Metadaten
Document Type:Article
Language:English
Author(s):Sebastian Sewerin, Lynn KaackORCiD, Joel Küttel, Fride Sigurdsson, Onerva Martikainen, Alisha Esshaki, Fabian Hafner
Parent Title (English):Scientific Data
Publication year:2023
Publishing Institution:Hertie School
DOI:https://doi.org/10.1038/s41597-023-02801-z
Release Date:2023/12/15
Volume:10
Article Number:896
Hertie School Research:Data Science Lab
AY 23/24:AY 23/24
Licence of document (German):Metadaten / metadata
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