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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...
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... we conducted an analysis of the correlation between temporal variables and transition time to explore the influence factors of transition pace. Because some samples have shorter transition times and the values of cumulative variables increase with the consumption of transition time, the correlation coefficients were calculated respectively based on the first five years and 10 years of history data from informal knowledge to formal knowledge, as shown in Table 3. ...Citations
Scientific knowledge evolution is an important signal for the innovative development of science and technology. As we know, new concepts and ideas are frequently born out of extensive recombination of existing concepts or notions. The evolution of a single knowledge unit or concept can be transformed into the formation of its ego-centered network from the perspective of combination innovation. Specifically, we proposed the eight research hypotheses from three aspects, namely, preferential attachment, transitivity, and homophily mechanisms. The 10,462 egocentric networks of scientific knowledge were extracted from knowledge co-occurrence network (KCN), and the Exponential Random Graph Models (ERGMs) were applied to model these sample networks individually, taking into account the influence of endogenous network structure and exogenous knowledge attribute variables. By conducting large-scale analytics on the fitting results, we found that (1) the degree centrality has a positive effect on knowledge evolution in the 99.9% sample networks, while the clustering coefficient contributes to the knowledge evolution in 56.8% sample networks at the 0.05 significance level; (2) the adoption behavior and domain impact of authors positively influence the scientific knowledge evolution, respectively, in the 93.5% and 80.8% sample networks; and (3) the knowledge type as well as the journal rank has an impact on the knowledge network evolution, demonstrating the homophily mechanism during the evolution of scientific knowledge.
Comprehending the patterns of knowledge evolution benefits funding agencies, policymakers, and researchers in developing creative ideas. We introduce the notation of scientific knowledge role transition as an evolution from informal to formal. We investigate how different factors affect the role transition of scientific knowledge, considering the two primary levels—transition pace and transition possibility. The interpretive machine learning models are conducted to discover that the Gradient Boosting classifier performs better for predicting transition possibility, and Random Forests regression is the most effective for predicting transition pace. Specifically, knowledge attribute features have a more obvious effect on the transition probability, while knowledge network structure has a greater effect on the transition pace. We further find that knowledge relatedness and citation number have negative effects on knowledge role transition, while adoption frequency, indegree centrality in the knowledge citation network, node number of the egocentric co-occurrence network, and journal impact of scientific knowledge have positive effects. The aforementioned discoveries enhance our comprehension of scientific knowledge evolution patterns and provide insight into the trajectory of scientific and technological advancement.
