标题： 将多元宇宙分析引入文献计量学：团队规模对颠覆性研究影响的案例研究 显示英文标题

标题： Introducing multiverse analysis to bibliometrics: The case of team size effects on disruptive research

摘要： 尽管文献计量学已成为评估研究表现的重要工具，但文献计量分析对一系列方法论选择非常敏感。数据选择、指标构建以及建模决策中的细微选择可能会显著改变结果。因此，确保稳健性——即研究发现能在不同合理的场景下成立——对于可信的研究和研究评估至关重要。为解决这一问题，本研究将多元宇宙分析引入文献计量学。多元宇宙分析是一种统计工具，使分析人员能够透明地讨论建模假设并全面评估模型的稳健性。与标准的稳健性检查通常仅涵盖所有可能模型的一小部分不同，多元宇宙分析包括所有可能的模型。我们通过检验吴等人（2019）提出的假设来展示多元宇宙分析的优势，该假设认为小团队比大团队产生更具颠覆性的研究成果。虽然我们发现了团队规模对颠覆性得分有负向影响的有力证据，但效应量如此之小，以至于其实际意义似乎值得怀疑。我们的研究结果强调了评估文献计量结果的多元宇宙稳健性以明确其实际意义的重要性。 显示英文摘要

摘要： Although bibliometrics has become an essential tool in the evaluation of research performance, bibliometric analyses are sensitive to a range of methodological choices. Subtle choices in data selection, indicator construction, and modeling decisions can substantially alter results. Ensuring robustness, meaning that findings hold up under different reasonable scenarios, is therefore critical for credible research and research evaluation. To address this issue, this study introduces multiverse analysis to bibliometrics. Multiverse analysis is a statistical tool that enables analysts to transparently discuss modeling assumptions and thoroughly assess model robustness. Whereas standard robustness checks usually cover only a small subset of all plausible models, multiverse analysis includes all plausible models. We illustrate the benefits of multiverse analysis by testing the hypothesis posed by Wu et al. (2019) that small teams produce more disruptive research than large teams. While we found robust evidence of a negative effect of team size on disruption scores, the effect size is so small that its practical relevance seems questionable. Our findings underscore the importance of assessing the multiverse robustness of bibliometric results to clarify their practical implications.