How Do We Engage with Other Disciplines? A Framework to Study Meaningful Interdisciplinary Discourse in Scholarly Publications
This work addresses the need for better tools to assess interdisciplinary discourse in scholarly publications, particularly for researchers and institutions promoting such work, though it is incremental as it builds on existing citation classification methods.
The authors tackled the problem of evaluating how interdisciplinary publications meaningfully engage with citations from other disciplines, proposing a tailored citation purpose taxonomy and annotation framework, and demonstrated its utility through an analysis of NLP and Computational Social Science publications.
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.