Temporal Representations of Citations for Understanding the Changing Roles of Scientific Publications

Abstract

Researchers may describe different aspects of past scientific publications in their publications and the descriptions may keep changing in the evolution of science. The diverse and changing descriptions (i.e., citation contexts) on a publication characterize the impact and contributions of the past publication. In this article, we aim to provide an approach to understanding the changing roles of a publication characterized by its citation contexts in the full text of publications. We proposed approaches for representing the changing citation contexts of cited publications in different periods as sequences of vectors by training temporal embedding models. We can utilize the temporal representations to quantify how much the roles of publications changed and interpret how they changed. We also evaluated the performance of three ways of constructing citation contexts for representation learning. Our study in the biomedical domain shows that our metric on the changes of publication roles is stable at the group level but it can account for the variation of individual publications.

Investigating disagreement in the scientific literature

“…Authors may disagree with more recent papers at different rates than older ones. We quantify disagreement based on the age of a cited paper papers (relative to the citing paper) and find that, on average, younger papers are more likely to feature in a disagreement citance than older ones, which may indicate that the role of cited literature varies based on its age ( He and Chen, 2018 ). Following a brief bump, or increase in disagreement (at 05–09 years), older papers tend to be receive fewer disagreement citances ( Figure 3—figure supplement 1 ), a pattern driven by field differences.…”

Responsible models and indicators: challenges from artificial intelligence

“…Meanwhile, the transformative advancements in artificial intelligence (AI), especially its horizontal applications in diverse sectors, such as ChatGPT, have been reshaping the cognitive processes and analytical paradigms of scientific research. Frontiers in Research Metrics and Analytics has actively embraced AI, e.g., machine learning-based classifiers and measures (Singhal et al, 2021 ; Mohlala and Bankole, 2022 ), embedding-based bibliometric studies (He and Chen, 2018 ; Wu et al, 2021 ), graph representation learning (Asada et al, 2021 ), and extensive utilization of academic graphs (Porter et al, 2020 ; Negro et al, 2023 ). Notebly, the community of Research Policy and Strategic Management has been actively engaged in in-depth discussions on trendy topics, such as responsible research and innovation (Buchmann et al, 2023 ), governance with equality and abundance (Kop, 2022 ), open data (Porter and Hook, 2022 ), and AI governance (Kalenzi, 2022 ).…”