构建跨学科引用评价框架，量化NLP与社科融合研究中的思想互动质量。
How Do We Engage with Other Disciplines? A Framework to Study Meaningful Interdisciplinary Discourse in Scholarly Publications
- 设计专用于跨学科研究的引用目的分类体系，结合人工标注验证。
- 发现多数跨学科引用仅作背景支撑，深度思想整合不足。
- 适合关注科研协作、跨学科评估的研究者与政策制定者参考。
随着跨学科研究日益流行及机构激励增强，理解学术出版物如何融合多领域思想变得愈发重要。现有计算方法（如作者机构多样性、关键词和引文模式）未能捕捉引用在推进本研究中的具体作用。尽管先前研究提出引文目的分类体系，但其不适用于跨学科场景，且缺乏引文互动质量的量化指标。为此，本文提出一个针对自然语言处理（NLP）与计算社会科学交叉领域的引文参与度评价框架。该方法构建了专为跨学科研究设计的引文目的分类体系，并通过注释研究加以支持。我们通过深入分析NLP与计算社会科学交汇处的出版物，展示了该框架的实用性。
原文摘要 · Abstract (English)
With the rising popularity of interdisciplinary work and increasing institutional incentives in this direction, there is a growing need to understand how resulting publications incorporate ideas from multiple disciplines. Existing computational approaches, such as affiliation diversity, keywords, and citation patterns, do not account for how individual citations are used to advance the citing work. Although, in line with addressing this gap, prior studies have proposed taxonomies to classify citation purpose, these frameworks are not well-suited to interdisciplinary research and do not provide quantitative measures of citation engagement quality. To address these limitations, we propose a framework for the evaluation of citation engagement in interdisciplinary Natural Language Processing (NLP) publications. Our approach introduces a citation purpose taxonomy tailored to interdisciplinary work, supported by an annotation study. We demonstrate the utility of this framework through a thorough analysis of publications at the intersection of NLP and Computational Social Science.
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