Star help and knowledge transfer: an event study analysis of star interactions observed from acknowledgement texts
利用自然语言处理技术分析三个小型开放经济体的论文致谢文本，发现获得明星科学家帮助能显著提升科研人员当年生产力，但持续效果需维持致谢联系；单次材料获取帮助即有持久效应，且对低生产力作者影响最大。
Abstract This paper contributes to the growing literature on the impact of connections to star scientists on the productivity of academic scientists. The existing literature generally focuses on larger economies and specific scientific fields in evaluating star-connection effects. It has rarely examined the particular channels through which stars have their effects. Using natural language processing (NLP) techniques to explore the acknowledgement texts of a broad corpus of published papers from three small open economies, we examine the effects of star help revealed by the acknowledgement texts published in articles. Using an event-study framework with matched data, we find evidence of an economically and statistically significant effect on scientist productivity in the year of acknowledgement of star help. However, there is only evidence of an enduring productivity effect if scientists maintain their acknowledgement of ties to the star over time. A similar pattern is evident across different types of acknowledgements, except for acknowledgements of star help with access to materials, which shows an enduring effect even after a single acknowledgement. The largest estimated star-help effects are found for authors in lower quartiles of the field-specific productivity distribution measured in the year before the help is acknowledged. The results are robust to using a raw-publications-based measure of scientist productivity in place of our preferred citation-weighted publications measure of productivity, suggesting that the observed productivity effect is unlikely to be due to a pure signalling effect. We discuss the implications of these findings for the design of star recruitment and integration policies.