The study presents a new approach for constructing an epistemological coordinate system that locates individual researchers within the disciplinary landscape of science. Drawing on a comprehensive national dataset of scientific outputs, we build a topic model based on a semantic network of publications and terms derived from textual content comprising titles, abstracts, and keywords. Compositional data transformation applied to the topic model enables a geometric analysis of topics across disciplines. The design yields four important results for addressing the gap between knowledge and knowledge-producers. (1) Hierarchical clustering confirms an alignment between traditional disciplinary classification and our empirical, bottom-up topic model. (2) Principal component analysis reveals three axes – Culture–Nature, Life–Non-life, and Materials–Methods – that primarily structure this scientific knowledge space. (3) The projection of individual researchers via their topic portfolios allows to locate them relationally on these three continuous measures of epistemological distinctions. (4) The robustness of our approach is validated by examining the links between researchers’ topic orientation and supplementary variables such as publication practices, gender, institutional affiliations, and funding sources. Our method could inform science policy and evaluation practices, as well as be extended to uncover associations between products and producers in other cultural fields.

中文翻译：

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绘制知识图谱：科学主题分析在学科环境中定位研究人员


该研究提出了一种构建认识论坐标系的新方法，该坐标系将个体研究人员定位在科学学科景观中。利用全面的国家科学成果数据集，我们基于出版物和术语的语义网络构建了一个主题模型，这些出版物和术语源自文本内容，包括标题、摘要和关键字。应用于主题模型的合成数据转换支持对跨学科的主题进行几何分析。该设计为解决知识生产和知识生产者之间的差距产生了四个重要结果。（1） 分层聚类证实了传统学科分类与我们自下而上的实证主题模型之间的一致性。（2） 主成分分析揭示了三个轴——文化-自然、生命-非生命和材料-方法——它们主要构建了这个科学知识空间。（3） 个体研究人员通过他们的主题组合进行投射，允许将他们关系定位在这三个连续的认识论差异度量上。（4） 通过检查研究人员的主题导向与补充变量（如出版实践、性别、机构隶属关系和资金来源）之间的联系，验证了我们方法的稳健性。我们的方法可以为科学政策和评估实践提供信息，并扩展到揭示其他文化领域的产品与生产者之间的关联。