Bibliometric Analysis

Bibliometric analysis studies how scientific knowledge develops by examining publications, citations, authorship, and scholarly text. Researchers use citation counts and network methods such as co-citation and bibliographic coupling, alongside topic modeling, embeddings, and language models, to trace research areas, collaborations, novelty, and influence. These methods are evaluated against expert judgments, annotated citation corpora, and observed patterns in fields such as artificial intelligence and biomedicine. A central challenge is that citation and impact measures are imperfect proxies for research quality, while data gaps and classification choices can shape the maps; combining auditable algorithms with human validation remains important.