论文标题
跨学科协作网络中的混合模式：通过多种镜头评估跨学科性
Mixing Patterns in Interdisciplinary Collaboration Networks: Assessing Interdisciplinarity Through Multiple Lenses
论文作者
论文摘要
跨学科研究合作存在固有的挑战，例如弥合认知差距和平衡交易成本与协作福利。这就提出了一个问题：跨学科研究是否一定会导致跨学科的合作？这项研究旨在探索这个问题，并通过检查协作网络中的混合模式来评估个人，二元和团队级别的跨学科研究的协作偏好。使用教育领域人工智能领域的2,000多个研究人员组成的网络，我们发现“跨学科性”是由个人研究人员的多样化研究经验证明的，而不是协作中研究人员之间的多样性。我们还通过采用一种新颖的方法来根据参与多个团队在协作网络中对积极和非活动的研究人员进行分类来检查组间混合。我们发现，活跃研究人员和非活动研究人员之间的学术表现和经验指标有显着差异，这表明组间混合是学术成功的关键因素。我们的结果阐明了跨学科研究中团队形成的性质，并强调了跨学科计划的重要性。
There are inherent challenges to interdisciplinary research collaboration, such as bridging cognitive gaps and balancing transaction costs with collaborative benefits. This raises the question: Does interdisciplinary research necessarily result in interdisciplinary collaborations? This study aims to explore this question and assess collaboration preferences in interdisciplinary research at the individual, dyadic, and team level by examining mixing patterns in a collaboration network. Using a network of over 2,000 researchers from the field of artificial intelligence in education, we find that "interdisciplinarity" is demonstrated by diverse research experiences of individual researchers rather than diversity among researchers within collaborations. We also examine intergroup mixing by applying a novel approach to classify the active and non-active researchers in the collaboration network based on participation in multiple teams. We find a significant difference in indicators of academic performance and experience between the clusters of active and non-active researchers, suggesting intergroup mixing as a key factor in academic success. Our results shed light on the nature of team formation in interdisciplinary research, as well as highlight the importance of interdisciplinary programs.
