学术知识：它是否反映了技术的组合式增长？
Academic Knowledge: Does it Reflect the Combinatorial Growth of Technology?
AI总结：
本文借鉴Koppl等人的组合增长理论，通过对经济学五大期刊的机器学习自然语言分析，发现学术职业制度抑制思想重组，导致研究前沿难以出现组合式增长。
摘要：
我探讨将增长植根于现有技术重组的这一概念，作为对工业革命期间所见证的显著增长的解释，这一概念最近由Koppl等人（2023）提出。我调整他们的组合增长理论，以评估其在大学和研究机构中生成学术知识的适用性，特别是在经济学领域。核心问题是，在学术界是否也能预期出现显著的组合式增长。当前的职业结构不鼓励思想、理论或方法的重组，使得早期职业研究人员坚持现状更为有利。我采用基于机器学习的自然语言分析，对经济学前5名期刊进行研究。分析揭示了过去三十年间主题之间的有限相关性，表明存在孤立主题岛，而非富有成效的重组。这证实了此前的理论考量。总体而言，学术界的制度秩序使得研究前沿的组合式增长不太可能发生。
英文摘要：
I explore the concept of growth being rooted in the recombination of existing technology as an explanation for the remarkable growth witnessed during the Industrial Revolution as it was recently proposed by Koppl et al.(2023). I adapt their combinatorial growth theory to assess its applicability in generating academic knowledge within universities and research institutions, particularly in the field of economics. The central question is whether significant combinatorial growth can also be anticipated in academia. The current career structures discourage the recombination of ideas, theories, or methods, making it more advantageous for early career researchers to stick to the status quo. I employ machine-learning-based natural language analysis of the top 5 journals in economics. The analysis reveals limited correlations between topics over the past three decades, suggesting the presence of isolated topic islands rather than productive recombination. This confirms the theoretical considerations beforehand. Overall, the institutional order of academia makes combinatorial growth at the research frontier unlikely.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
