Using novel data and ensemble models to improve automated labeling of Sustainable Development Goals
Dirk U Wulff1,2, Dominik S Meier1, Rui Mata1
1Max Planck Institute for Human Development, Berlin, Germany.
Summary
Different text-based systems for labeling United Nations Sustainable Development Goals (SDGs) vary in accuracy and bias. An ensemble model combining multiple systems offers improved performance for monitoring SDG progress.
Area of Science:
- Computational Social Science
- Data Science
- Policy Analysis
Background:
- Text-based labeling systems are crucial for monitoring progress on the United Nations Sustainable Development Goals (SDGs).
- Existing systems exhibit variability in performance and potential biases, impacting the reliability of SDG progress assessments.
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