比较四种方法，找跨学科研究中的神经科学与计算机科学交叉点。
Comparing Retrieval Strategies to Capture Interdisciplinary Scientific Research: A Bibliometric Evaluation of the Integration of Neuroscience and Computer Science
- 用关键词和引文模式设计四种检索策略
- 基于参考文献的方法召回率和准确率更高
- 方法可推广至其他跨学科领域研究
跨学科科研在知识生产、资助政策和学术交流中日益重要。尽管多数研究基于预设领域进行关键词搜索，但很少探索两个独立领域间自然形成的交叉点。本文旨在开发并比较构建两领域交叉研究数据库的方法。以神经科学与计算机科学的交汇为例，提出并对比四种检索策略：两种基于关键词，两种基于引文与参考文献模式。结果表明，基于参考文献的策略在检索效果、伪召回率和F1值上表现更优。本研究虽聚焦于神经科学与计算机科学的交叉分析，其方法论对广泛跨学科领域具有普适意义。
原文摘要 · Abstract (English)
Interdisciplinary scientific research is increasingly important in knowledge production, funding policies, and academic discussions on scholarly communication. While many studies focus on interdisciplinary corpora defined a priori -- usually through keyword-based searches within assumed interdisciplinary domains -- few explore interdisciplinarity as an emergent intersection between two distinct fields. Thus, methodological proposals for building databases at the intersection of two fields of knowledge are scarce. The goal of this article is to develop and compare different strategies for defining an interdisciplinary corpus between two bodies of knowledge. As a case study, we focus on the intersection between neuroscience and computer science. To this end, we develop and compare four retrieval strategies, two of them based on keywords and two based on citation and reference patterns. Our results show that the reference-based strategy provides better retrieval, pseudorecall, and F1. While we focus on comparing strategies for the study of the intersection between the fields of neuroscience and computer science, this methodological reflection is applicable to a wide range of interdisciplinary domains.
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