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arXiv · 2601.06038

Developing Bayesian probabilistic reasoning capacity in HSS disciplines: Qualitative evaluation on bayesvl and BMF analytics for ECRs

Quan-Hoang Vuong·Minh-Hoang Nguyen
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Abstract

Methodological innovations have become increasingly critical in the humanities and social sciences (HSS) as researchers confront complex, nonlinear, and rapidly evolving socio-environmental systems. On the other hand, Early Career Researchers (ECRs) continue to face intensified publication pressure, limited resources, and persistent methodological barriers. Employing the GITT-VT analytical paradigm--which integrates worldviews from quantum physics, mathematical logic, and information theory--this study examines the seven-year evolution of the Bayesian Mindsponge Framework (BMF) analytics and the bayesvl R software (hereafter referred to collectively as BMF analytics) and evaluates their contributions to strengthening ECRs' capacity for rigorous and innovative research. Since 2019, the bayesvl R package and BMF analytics have supported more than 160 authors from 22 countries in producing 112 peer-reviewed publications spanning both qualitative and quantitative designs across diverse interdisciplinary domains. By tracing the method's inception, refinement, and developmental trajectory, this study elucidates how accessible, theory-driven computational tools can lower barriers to advanced quantitative analysis, foster a more inclusive methodological ecosystem--particularly for ECRs in low-resource settings--and inform the design of next-generation research methods that are flexible, reproducible, conceptually justified, and well-suited to interdisciplinary inquiries.

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Quan-Hoang Vuong, Minh-Hoang Nguyen. 2026-07-01. Developing Bayesian probabilistic reasoning capacity in HSS disciplines: Qualitative evaluation on bayesvl and BMF analytics for ECRs. https://arxiv.org/abs/2601.06038

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