AI对公共管理学术的分类存在偏见，影响学科边界认知。
When AI Classifies: What Counts as Public Administration?
- 用五种方法对比文献数据，发现算法分类差异大
- 不同方法识别出完全不重叠的论文和期刊
- 提醒警惕算法偏见，人类判断仍不可或缺
本研究探讨了不同学术表征体系如何识别和刻画广义公共管理（PA）及人工智能相关公共管理（AI-in-PA）研究。基于Web of Science与OpenAlex，比较了作者自定义、引文驱动与AI辅助等五种方法。结果显示，在文献规模、出版类型、发表平台、时间演变及主题聚类结构上存在显著差异。不同方法常识别出不同知识领域，而非同一学术群体的子集，表现为各表征间无论文与出版物重合。研究揭示算法化知识组织正深刻影响跨学科研究的分类、结构与理解方式，从认识论层面重塑其可见性、知识结构与边界界定。AI驱动的学术分类并非中立，而是具有解释性、可能自我强化，并潜在限制学科边界的演化与适应。人类的学科判断至关重要，应被补充而非取代。
原文摘要 · Abstract (English)
This study examines how alternative systems of scholarly representation identify and characterize broad public administration (PA) and artificial intelligence related public administration (AI-in-PA) scholarship. Using Web of Science and OpenAlex, it compares five approaches based on author-defined, citation-driven, and AI-assisted representations. The results highlight substantial differences in corpus size, publication types, publishing outlets, temporal development, and thematic clustering and structure. The alternative approaches often identify different knowledge domains instead of varied subsets of the same scholarship and therefore produce distinct representations, as evidenced by no overlap in publications and publishing outlets across representations. The findings suggest that algorithmic knowledge organization increasingly influences how interdisciplinary scholarship is classified, structured, and understood and, epistemologically, how its visibility, intellectual structure, and boundaries are represented. AI-enabled scholarly classifications and representations are not neutral but interpretative, likely self-reinforcing, and potentially constrain the evolution and adaptation of disciplinary boundaries. Human disciplinary judgment is essential and is complemented rather than replaced.
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