arXiv:2607.05420v1 Announce Type: cross Abstract: 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 iden
The proliferation of AI applications into governance and public administration is driving a critical need to standardize how these phenomena are understood and categorized.
How AI's role in public administration is classified and understood directly impacts policy, funding allocation, ethical frameworks, and public perception, shaping its future development and integration.
This study's findings indicate that different scholarly representation systems yield substantially varied understandings of AI's presence in public administration, necessitating a more unified, robust classification approach.
- · AI governance researchers
- · Public administration scholars
- · Policymakers
- · Research institutions adopting comprehensive classification systems
- · Fragmented research methodologies
- · Organizations relying on narrow AI classification systems
- · Stakeholders misinterpreting AI's actual impact in public administration
Increased debate and development of standardized classification taxonomies for AI's role in public administration across academic and governmental bodies.
Improved clarity in policy discussions and regulatory frameworks concerning AI in public services, leading to more informed and effective governance.
The establishment of international benchmarks for evaluating AI's efficacy and ethical implications within public administration, influencing global adoption patterns.
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Read at arXiv cs.AI