This study quantifies and analyzes the individual-level abilities of scientists utilizing either an exploration or an exploitation strategy. Specifically, we present a Research Strategy Q model, which untangles the coupling effect of scientists’ research ability (Q α ) and research strategy ability ( E α π ) on research performance. Q α indicates scientists’ fundamental ability to publish high-quality papers, while E α π indicates scientists’ proficiency in terms of exploration and exploitation strategies. Five research strategies proposed by our previous study are employed. We generate synthetic data and collect empirical data as our experimental data set. We show that these research strategies present different benefit and risk characteristics. Adopting some exploitation strategies tends to stifle research performance, while exploration strategies are high risk and high yield. Q α and E α π have predictive power for research performance. Moreover, we find that, first, scholars who prefer to execute a research strategy, π , may not necessarily be better at executing π . Second, some scholars have differences in their abilities towards different strategies, while other scholars have differences in their abilities towards the same strategy. Third, exploration and exploitation are not contradictory but complementary from the perspective of proficiency, while they are mutually exclusive from the perspective of selection preference.

Topic analysis aims to study topic evolution and trends in order to help researchers understand the process of knowledge evolution and creation. This paper develops a novel topic evolution analysis framework, which we use to demonstrate, forecast, and explain topic evolution from the perspective of the geometrical motion of topic embeddings generated by pretrained language models. Our data set comprises approximately 15 million papers in the computer science field, with 7,000 “fields of study” to represent the topics. First, we demonstrate that over 80% of topics have undergone obvious motion in the semantic vector space, based on the hyperplane and its normal vector generated by a support vector machine. Subsequently, we verified the predictability of the motion based on three vector regression models by predicting topic embeddings. Finally, we employed a decoder to explain the predicted motion, whose forecast embeddings can capture about 50% of unseen topics. Our research framework shows that topic evolution can be analyzed via the geometrical motion of topic embeddings, and the semantic motion of old topics nurtures new topics. The current study opens new research pathways in topic analysis and sheds light on the topic evolution mechanism from a novel geometric perspective.

Informal knowledge constantly transitions into formal domain knowledge in the dynamic knowledge base. This article focuses on an integrative understanding of the knowledge role transition from the perspective of knowledge codification. The transition process is characterized by several dynamics involving a variety of bibliometric entities, such as authors, keywords, institutions, and venues. We thereby designed a series of temporal and cumulative indicators to respectively explore transition possibility ( whether new knowledge could be transitioned into formal knowledge ) and transition pace ( how long it would take ). By analyzing the large-scale metadata of publications that contain informal knowledge and formal knowledge in the PubMed database, we find that multidimensional variables are essential to comprehensively understand knowledge role transition. More significantly, early funding support is more important for improving transition pace; journal impact has a positive correlation with the transition possibility but a negative correlation with transition pace; and weaker knowledge relatedness raises the transition possibility, whereas stronger knowledge relatedness improves the transition pace.