Spanish-language text classification for environmental evidence synthesis using multilingual pre-trained models.

ABSTRACT: Artificial intelligence (AI) is increasingly being explored as a tool to optimize and accelerate various stages of evidence synthesis. A persistent challenge in environmental evidence syntheses is that these remain predominantly monolingual (English), leading to biased results and misinforming cross-scale policy decisions. AI offers a promising opportunity to incorporate non-English language evidence in evidence syntheses screening process and help to move beyond the current monolingual focus of evidence syntheses. Using a corpus of Spanish-language peer-reviewed papers on biodiversity conservation interventions, we developed and evaluated text classifiers using supervised machine learning models. Our best-performing model achieved 100% recall meaning no relevant papers (n = 9) were missed

SUBMITTER: Berdejo-Espinola V 

PROVIDER: S-EPMC12613578 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature