Abstract
Scientific digital libraries speed dissemination of scientific publications, but also the propagation of invalid or unreliable knowledge. Although many papers with known validity problems are highly cited, no auditing process is currently available to determine whether a citing paper's findings fundamentally depend on invalid or unreliable knowledge. To address this, we introduce a new framework, the keystone framework, designed to identify when and how citing unreliable findings impacts a paper, using argumentation theory and citation context analysis. Through two pilot case studies, we demonstrate how the keystone framework can be applied to knowledge maintenance tasks for digital libraries, including addressing citations of a non-reproducible paper and identifying statements most needing validation in a high-impact paper. We identify roles for librarians, database maintainers, knowledgebase curators, and research software engineers in applying the framework to scientific digital libraries.
Highlights
Scientific digital libraries make the dissemination of scientific publications easier and faster
This work is motivated by the questions: Does it matter when citing authors make use of a paper whose findings are no longer considered valid? Are papers citing it necessarily wrong? Our work introduces a framework for addressing these questions by combining argumentation theory and citation context analysis, pilot tests our new framework in two case studies, and suggests future directions for applying the framework
We focus on keystone citation contexts, which we define as citation contexts supporting keystone statements
Summary
Scientific digital libraries make the dissemination of scientific publications easier and faster. This facilitates the propagation of invalid or unreliable knowledge. Citations to invalidated papers pose a threat to scientific knowledge maintenance. Currently, these citations are not flagged for review, and no auditing process is available to determine whether a new paper’s findings fundamentally depend on invalid or unreliable knowledge. Our goal in this paper is to set an agenda for knowledge maintenance in scientific digital libraries. This work is motivated by the questions: Does it matter when citing authors make use of a paper whose findings are no longer considered valid? This work is motivated by the questions: Does it matter when citing authors make use of a paper whose findings are no longer considered valid? Are papers citing it necessarily wrong? Our work introduces a framework for addressing these questions by combining argumentation theory and citation context analysis, pilot tests our new framework in two case studies, and suggests future directions for applying the framework
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