A Novel Kuhnian Ontology for Epistemic Classification of STM Scholarly Articles
This offers a transparent and auditable tool for funding and policy decisions in the scientific community, though it builds incrementally on prior work.
The authors tackled the problem of opaque research evaluation by developing KGX3, a deterministic model for classifying scholarly articles into Kuhnian stages, which provides early signals of paradigm shifts with operational complexity at global scale.
Despite rapid gains in scale, research evaluation still relies on opaque, lagging proxies. To serve the scientific community, we pursue transparency: reproducible, auditable epistemic classification useful for funding and policy. Here we formalize KGX3 as a scenario-based model for mapping Kuhnian stages from research papers, prove determinism of the classification pipeline, and define the epistemic manifold that yields paradigm maps. We report validation across recent corpora, operational complexity at global scale, and governance that preserves interpretability while protecting core IP. The system delivers early, actionable signals of drift, crisis, and shift unavailable to citation metrics or citations-anchored NLP. KGX3 is the latest iteration of a deterministic epistemic engine developed since 2019, originating as Soph.io (2020), advanced as iKuhn (2024), and field-tested through Preprint Watch in 2025.