← 研踪 PaperTrailarXiv:2611.11663 · v1
The AI research space shapes country academic performance
首次提交 2026-11-09 06:41
版本更新 2026-11-09 06:41
雷达观测 2026-11-10 13:19
摘要
Recent advances in Artificial Intelligence (AI) have demonstrated great potential across diverse domains, prompting increasing attention to understanding the AI ecosystem. As knowledge production is often perceived as a process of connecting existing pieces of knowledge, mapping the interconnections of AI knowledge is crucial for unraveling the dynamics of scientific production in AI. However, maps created so far have fallen short of capturing knowledge relatedness, focusing instead on citation relationships between subfields. Here, we build an AI research space by connecting pairs of AI research topics based on their proximity measured through author-topic coupling. Projections of country level performances onto the space reveal that countries exhibit distinct baskets of specializations. We further examine the role of the structure of the AI research space in the scientific development of nations. We find that countries are more likely to gain specialization in topics that have high proximity to already specialized neighbor topics, and that publications whose topics align more with the developed topics in the country are more likely to become high impact. Our results highlight the AI development gaps across nations and the path-dependence in the development induced by the AI research space.
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v1 · 2026-11-09 06:41 · 最新观测版本
The AI research space shapes country academic performance
Recent advances in Artificial Intelligence (AI) have demonstrated great potential across diverse domains, prompting increasing attention to understanding the AI ecosystem. As knowledge production is often perceived as a process of connecting existing pieces of knowledge, mapping the interconnections of AI knowledge is crucial for unraveling the dynamics of scientific production in AI. However, maps created so far have fallen short of capturing knowledge relatedness, focusing instead on citation relationships between subfields. Here, we build an AI research space by connecting pairs of AI research topics based on their proximity measured through author-topic coupling. Projections of country level performances onto the space reveal that countries exhibit distinct baskets of specializations. We further examine the role of the structure of the AI research space in the scientific development of nations. We find that countries are more likely to gain specialization in topics that have high proximity to already specialized neighbor topics, and that publications whose topics align more with the developed topics in the country are more likely to become high impact. Our results highlight the AI development gaps across nations and the path-dependence in the development induced by the AI research space.
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