{"ID":2824791,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.22524","arxiv_id":"2512.22524","title":"Periodical embeddings uncover hidden interdisciplinary patterns in the subject classification scheme of science","abstract":"Subject classification schemes are foundational to the organization, evaluation, and navigation of scientific knowledge. While expert-curated systems like Scopus provide widely used taxonomies, they often suffer from coarse granularity, subjectivity, and limited adaptability to emerging interdisciplinary fields. Data-driven alternatives based on citation networks show promise but lack rigorous, external validation against the semantic content of scientific literature. Here, we propose a novel quantitative framework that leverages classification tasks to evaluate the effectiveness of journal classification schemes. Using over 23 million paper abstracts, we demonstrate that labels derived from k-means clustering on Periodical2Vec (P2V)--a periodical embedding learned from paper-level citations--yield significantly higher classification performance than both Scopus and other data-driven baselines (e.g., citation, co-citation, and Node2Vec variants). By comparing journal partitions across classification schemes, two structural patterns emerge on the map of science: (1) the reorganization of disciplinary boundaries--splitting overly broad categories (e.g., \"Medicine\" into \"Oncology\", \"Cardiology\", and other specialties) while merging artificially fragmented ones (e.g., \"Chemistry\" and \"Chemical Engineering\"); and (2) the identification of coherent interdisciplinary clusters--such as \"Biomedical Engineering\", \"Medical Ethics\", and \"Information Management\"--that are dispersed across multiple categories but unified in citation space. These findings underscore that citation-derived periodical embeddings not only outperform traditional taxonomies in predictive validity but also offer a dynamic, fine-grained map of science that better reflects both the specialization and interdisciplinarity inherent in contemporary research.","short_abstract":"Subject classification schemes are foundational to the organization, evaluation, and navigation of scientific knowledge. While expert-curated systems like Scopus provide widely used taxonomies, they often suffer from coarse granularity, subjectivity, and limited adaptability to emerging interdisciplinary fields. Data-d...","url_abs":"https://arxiv.org/abs/2512.22524","url_pdf":"https://arxiv.org/pdf/2512.22524v1","authors":"[\"Zhuoqi Lyu\",\"Qing Ke\"]","published":"2025-12-27T08:58:23Z","proceeding":"cs.DL","tasks":"[\"cs.DL\",\"cs.SI\",\"physics.soc-ph\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}