Discourse Patterns in Sustainable Development Partnerships: An Unsupervised Machine Learning Analysis of the GENESIS Multistakeholder Partnership Database

Multistakeholder partnerships (MSPs) are central to the 2030 Agenda for Sustainable Development, yet the UN Partnership Platform suffers from an extreme validation asymmetry: fewer than 5% of registered projects undergo independent verification. This study examines whether discourse patterns distinguish validated from non-validated MSPs. Applying BERTopic neural topic modeling to 3807 project descriptions in the GENESIS WP4 Database, we identify three substantive thematic clusters, spanning climate, sanitation, and health; marine and fisheries; and sustainable textiles, alongside a combined language–artifact cluster excluded from thematic interpretation. The target topic count was fixed to ensure reproducibility after an initial automatic-selection step proved unstable across runs; a multi-seed check confirms stable topic counts with moderate assignment-level agreement (mean Adjusted Rand Index = 0.63). We introduce the Validated-Discourse Similarity Index (VDSI), a leave-one-out cosine similarity measure comparing each project’s textual embedding to a centroid of validated MSPs shown to be more homogeneous than random samples of non-validated projects (p = 0.002). VDSI analysis indicates that 3538 of the 3807 projects (92.9%) exhibit Inconsistent Non-Validated language resembling validated MSPs despite lacking independent verification, though raw semantic similarity alone only modestly discriminates validation status (AUC = 0.686), indicating a real but partial signal rather than a proxy for validation. Partner count is the strongest structural discriminator of validated MSPs (r = −0.389, p < 0.001), remaining significant after adjusting for SDG scope, description length, duration, topic, and language (adjusted OR = 1.38 per SD, p < 0.001), and consistent across six SDG-level subgroups. These findings extend SDG-washing scholarship to the UN multilateral voluntary commitment ecosystem and offer a provisional, discourse-informed basis for partnership evaluation.