{
  "annotation_id": "ae82bc41-cf7f-425c-bc35-fee82761e406",
  "date_created": "2026-02-17T05:53:04.381000Z",
  "date_modified": "2026-02-17T05:53:04.381000Z",
  "file_hash": "81a747a9d648aa6fcc86fcee72056473baa3f5bc46ebe6392708395aec875cd0",
  "private": false,
  "record": {
    "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, while 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\u0027 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\u0027s 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.",
    "arxiv_id": "2601.06038",
    "authors": [
      "Quan-Hoang Vuong",
      "Minh-Hoang Nguyen"
    ],
    "categories": [
      "cs.CY"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Developing Bayesian probabilistic reasoning capacity in HSS disciplines: Qualitative evaluation on bayesvl and BMF analytics for ECRs",
    "url": "https://arxiv.org/abs/2601.06038",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "23c1bc59-f5ad-4856-9c80-b9bcc9490215",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}