When AI Classifies: What Counts as Public Administration?
📰 ArXiv cs.AI
Learn how AI classifies public administration research and the differences in scholarly representation approaches
Action Steps
- Collect data from Web of Science and OpenAlex to compare scholarly representation approaches
- Apply author-defined, citation-driven, and AI-assisted representations to identify PA and AI-in-PA research
- Analyze the results to highlight differences in corpus size, publication types, and publishing outlets
- Compare the effectiveness of each approach in characterizing PA and AI-in-PA scholarship
- Use the findings to inform the development of more accurate AI classification systems for PA research
Who Needs to Know This
Researchers and academics in public administration and AI can benefit from understanding the different approaches to identifying and characterizing PA and AI-in-PA scholarship
Key Insight
💡 Different scholarly representation approaches can lead to substantial differences in the identification and characterization of public administration research
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🤖 AI classification of public administration research: which approach is most effective? 📊
Key Takeaways
Learn how AI classifies public administration research and the differences in scholarly representation approaches
Full Article
Title: When AI Classifies: What Counts as Public Administration?
Abstract:
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
Abstract:
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
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