Reading between the lines: The intersection of research attention and sustainable development goals

Abstract

AbstractIn September 2015, the United Nations (UN) adopted 17 Sustainable Development Goals (SDGs) to transform our world by 2030. The scientific discourse around these SDGs has expanded rapidly since then, highlighting the need for efficient analysis of the large amount of textual data using Natural Language Processing. Our research addresses this need by employing a zero‐shot text classification for SDG‐related scientific articles, which allows for a thorough examination of scholarly discourse and the relationship between research attention and SDG achievement. We introduce the Research Attention Index (RAI), a novel metric that quantifies the research attention each SDG receives within a specific country. Our study contributes to the existing literature by providing a holistic view of global research attention to the SDGs. It also demonstrates the effectiveness of zero‐shot text classification for large‐scale textual labeling, and underlines the relevance of abstract analysis in understanding SDG‐related discourse. Moreover, we examine the (non)‐linear relationship between the RAI and SDG achievement across countries. Our results indicate considerable variations in the scientific discourse across countries worldwide and reveal a complex, non‐linear relationship between research attention and progress towards achieving the SDGs. This underscores the importance of understanding the dynamics between research attention and sustainable development outcomes.