当 AI 进行学科分类时：什么才算公共行政研究？

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Abstract:This study examines how alternative systems of scholarly representation identify and characterize public administration (PA) and artificial intelligence-related PA (AI-in-PA) scholarship. The central question is not which system provides a definitive classification of PA, but how alternative systems of scholarly classification differ in identifying and representing its intellectual boundaries. With an exploratory and comparative analysis of Web of Science and OpenAlex, the study compares five approaches based on author-defined, citation-driven, and AI-assisted representations. The results highlight substantial differences and limited overlap in corpus size, publication type, DOI availability, publishing outlets, temporal patterns, and thematic structure. The alternative approaches tend to produce distinct representations because they identify substantially different bodies of scholarship rather than merely larger or smaller subsets of the same literature. The findings suggest that algorithmic knowledge organization influences how interdisciplinary scholarship is classified, connected, and understood and, epistemologically, how its visibility, structure, and intellectual identity are represented. AI-enabled scholarly classifications are not neutral but interpretative, potentially self-reinforcing, and may constrain the evolution and adaptation of disciplinary boundaries. Human expert judgment therefore remains essential and is complemented rather than replaced by AI-assisted scholarly classification.

Subjects:	Digital Libraries (cs.DL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Databases (cs.DB)
Cite as:	arXiv:2607.05420 [cs.DL]
	(or arXiv:2607.05420v2 [cs.DL] for this version)
	https://doi.org/10.48550/arXiv.2607.05420 arXiv-issued DOI via DataCite

Submission history

From: Shaoming Cheng [view email]
[v1] Fri, 26 Jun 2026 20:44:17 UTC (4,733 KB)
[v2] Wed, 11 Nov 2026 19:18:02 UTC (1,368 KB)