知识和社会相关性塑造研究组合多样化
Knowledge and Social Relatedness Shape Research Portfolio Diversification
摘要：
科学发现是由科学家的选择，从而由他们的职业模式。在科学前沿工作所需的知识越来越多，这使得个人更难走上未经探索的道路。然而，合作可以减少学习成本-尽管以增加协调成本为代价。在这篇文章中，我们使用了一个非常大的样本物理学家的出版历史的数据来衡量他们的多元化战略的知识和社会关系的影响。使用二分网络，我们计算的主题相似性和社会接近度的措施。我们发现，科学家的策略不是随机的，他们受到两者的显著影响。跨主题的知识相关性解释了逻辑回归偏差和社会相关性的$\approx 10\%$和$\approx 30\%$，这表明科学是一个突出的社会企业：当科学家们走出他们的核心专业，他们这样做是通过合作。有趣的是，我们还发现知识和社会关系之间存在显著的负向相互作用，这表明科学家离专业化越远，他们就越依赖合作。我们的研究结果为科学多样化战略的更广泛的定量分析提供了一个起点，这也可以扩展到技术创新领域-从比较和政策角度提供见解。
英文摘要：
Scientific discovery is shaped by scientists' choices and thus by their career patterns. The increasing knowledge required to work at the frontier of science makes it harder for an individual to embark on unexplored paths. Yet collaborations can reduce learning costs -- albeit at the expense of increased coordination costs. In this article, we use data on the publication histories of a very large sample of physicists to measure the effects of knowledge and social relatedness on their diversification strategies. Using bipartite networks, we compute a measure of topics similarity and a measure of social proximity. We find that scientists' strategies are not random, and that they are significantly affected by both. Knowledge relatedness across topics explains $\approx 10\%$ of logistic regression deviances and social relatedness as much as $\approx 30\%$, suggesting that science is an eminently social enterprise: when scientists move out of their core specialization, they do so through collaborations. Interestingly, we also find a significant negative interaction between knowledge and social relatedness, suggesting that the farther scientists move from their specialization, the more they rely on collaborations. Our results provide a starting point for broader quantitative analyses of scientific diversification strategies, which could also be extended to the domain of technological innovation -- offering insights from a comparative and policy perspective.
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。
