Mapping human security strategies to sustainable development goals: a machine learning approach

This paper introduces a machine learning (ML) based approach for integrating Human Security (HS) and Sustainable Development Goals (SDGs). Originating in the 1990s, HS focuses on strategic, people-centric interventions for ensuring comprehensive welfare and resilience. It closely aligns with the SDGs, together forming the foundation for global sustainable development initiatives. Our methodology involves a structured analysis of the relationship between 44 HS reports and the SDGs using advanced text analytics and machine learning methods, each report was evaluated based on its thematic relevance to HS aspects and SDG goals and sub-targets using a keyword-based matching approach. The data retrieved from the HS-related reports was transformed into a numerical format using the Term Frequency-Inverse Document Frequency (TF-IDF) method and classified through the Random Forest Classifier. Advanced models were utilized for exploration, namely BERT (Devlin et al. in Polit Sci, 2018) DistilBERT (Sanh et al. in ArXiv preprint arXiv: 1910.01108, 2019), and ELECTRA (Clark et al. in Polit Sci, 2020). This process contributed to the development of a web-based SDG mapping tool, specifically tailored for elaborating on the HS-SDG nexus. enabling the analysis of 13 new reports and their connections to the SDGs. This web application provides a streamlined interface for analyzing documents in PDF form and determining their relevance to the SDGs, allowing users to upload PDF files via an HTML form. Following expert consultation, our results outperform currently available similar online tools and assist in establishing strong links between the reports and global objectives, offering a nuanced understanding of the interplay between HS and sustainable development. This research provides a scalable framework to explore the relationship between HS and the global sustainability agenda, offering a practical, efficient resource for scholars and policymakers.