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From informal to formal: scientific knowledge role transition prediction

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Abstract

Comprehending the patterns of knowledge evolution benefits funding agencies, policymakers, and researchers in develo** 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.

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Notes

  1. https://doi.org/10.6084/m9.figshare.24082668.v2.

  2. https://github.com/shap.

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Acknowledgements

This work was supported by Youth Program of the National Natural Science Foundation of China (Grant No. 72304108), Major Program of the National Fund of Philosophy and Social Science of China (Grant No.19ZDA345), Natural Science Foundation of Hubei Province (Grant No. 2024AFB1018) and the Fundamental Research Funds for the Central Universities (Grant No. CCNU24ZZ140).

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Authors and Affiliations

  1. School of Information Management, Central China Normal University, Wuhan, 430079, China

    **qing Yang

  2. Department of Information Management, Peking University, Bei**g, 100871, China

    Zhifeng Liu

  3. School of Information Management, Wuhan University, Wuhan, 430072, China

    Yong Huang

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  1. **qing Yang
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Correspondence to **qing Yang.

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Appendix A

Appendix A

See Tables 5 and 6, Figs. 13 and 14.

Table 5 Hyperparameter settings for the model predicting possibility of knowledge role transition
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Table 6 Hyperparameter settings for the model predicting the pace of knowledge role transition
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Fig. 13
figure 13

The scatter plot of the top-6 features for predicting transition possibility in Group 2

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Fig. 14
figure 14

The scatter plot of the top-6 features for predicting transition possibility in Group3

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Yang, J., Liu, Z. & Huang, Y. From informal to formal: scientific knowledge role transition prediction. Scientometrics (2024). https://doi.org/10.1007/s11192-024-05093-1

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