超越成对距离：认知遍历距离作为科学新颖性的整体度量
Beyond Pairwise Distance: Cognitive Traversal Distance as a Holistic Measure of Scientific Novelty
AI总结：
本文提出认知遍历距离（CTD）作为科学新颖性的度量，通过知识网络的最短路径长度评估论文整合多知识单元的结构距离，优于传统聚合方法。
摘要：
科学新颖性是引文计量学中的关键概念，通常通过聚合论文所涉及的知识单元之间的成对距离来衡量。尽管先前工作已经优化了此类距离的计算方式，但对如何将二元关系聚合以表征论文层面的新颖性却关注不足。我们通过引入基于网络的指标，即认知遍历距离（CTD）来解决这一局限。将历史文献视为加权知识网络，CTD被定义为连接论文相关所有知识单元所需最短路径的长度。CTD提供了一种论文层面的新颖性度量，反映了整合多个知识单元所需的最小结构距离，超越了基于均值或分位数的成对距离聚合。使用2700万篇生物医学出版物，这些出版物通过OpenAlex和医学主题词（MeSH）标准化为知识单元，我们评估CTD与基于专家的新颖性基准，如F1000Prime推荐论文和诺贝尔奖获奖论文。CTD一致优于传统聚合指标。我们进一步表明，基于MeSH的CTD对由完全新概念标签出现驱动的新颖性更不敏感，澄清了其与最近基于文本的度量相比的范围。
英文摘要：
Scientific novelty is a critical construct in bibliometrics and is commonly measured by aggregating pairwise distances between the knowledge units underlying a paper. While prior work has refined how such distances are computed, less attention has been paid to how dyadic relations are aggregated to characterize novelty at the paper level. We address this limitation by introducing a network-based indicator, Cognitive Traversal Distance (CTD). Conceptualizing the historical literature as a weighted knowledge network, CTD is defined as the length of the shortest path required to connect all knowledge units associated with a paper. CTD provides a paper-level novelty measure that reflects the minimal structural distance needed to integrate multiple knowledge units, moving beyond mean- or quantile-based aggregation of pairwise distances. Using 27 million biomedical publications indexed by OpenAlex and Medical Subject Headings (MeSH) as standardized knowledge units, we evaluate CTD against expert-based novelty benchmarks from F1000Prime-recommended papers and Nobel Prize-winning publications. CTD consistently outperforms conventional aggregation-based indicators. We further show that MeSH-based CTD is less sensitive to novelty driven by the emergence of entirely new conceptual labels, clarifying its scope relative to recent text-based measures.
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