INFORMATION RESEARCH

Digital Literacy Policy Framework Evolution and Its Causal Reasoning: New Evidence from Complex Network and Machine Learning

  • Liu Chunnian ,
  • Zhang Yunliang ,
  • Yi Lan
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  • 1 School of Public Policy and Management, Nanchang University, Nanchang 330031;
    2 Digital Literacy and Skills Enhancement Research Center, Jiangxi Province Philosophy and Social Science Key Research Base, Nanchang University, Nanchang 330031

Received date: 2023-11-20

  Revised date: 2024-02-06

  Online published: 2024-06-19

Supported by

This work is supported by the National Natural Science Foundation of China project titled “Research on the Fluctuation Mechanism and Optimization of Users’ Information Demand Considering the Role of Dual Factors of Media and Crisis Types” (Grant No. 72064027) and “Mechanism of Synergy between Emergence of Digital Literacy among Returning Migrant Workers and Diffusion of Digital Technology in Promoting Rural Revitalization under Complex Social Network with Hierarchical Layers” (Grant No. 72364024).

Abstract

[Purpose/Significance] This paper analyzes China’s digital literacy policy, excavates the correlation and causal relationship of policy elements in its evolution, explores the governance path of digital literacy improvement, and provides reference for promoting the optimization and improvement of digital literacy policy. [Method/Process] Through the policy text analysis process on grounded theory, Bayesian complex network and machine learning method, it collected 1087 policy texts related to digital literacy in China, and constructed the policy framework. On the basis of it, it analyzed the evolution of policy elements, policy tool-target causal reasoning with Bayesian complex network and random forest method, and summarized the promotion path of national digital literacy. [Result/Conclusion] The main body of China’s digital literacy policy includes three categories: party organization, government subject and government external subject. The policy tools can be divided into five types: empowerment, training, stimulation, support and implementation. The first type is the core dimension and has the most important influence on the coupling coordination degree of policy objectives. The second always maintains the greatest positive correlation with the policy objectives in each period, and has the most important influence on the comprehensive score of the policy objectives. The status of stimulation tool is rising but its potential has not been effectively explored. In the emergence stage, the central node of the policy network changes from the empowerment to the implementation with the most closely relation to the support. In the future, it is necessary to accurately grasp the characteristics of different policy tools, and promote the improvement of digital literacy by optimizing the combination of tools, gathering multiple forces, promoting literacy education, developing digital economy, carrying out digital assistance and improving policy effectiveness.

Cite this article

Liu Chunnian , Zhang Yunliang , Yi Lan . Digital Literacy Policy Framework Evolution and Its Causal Reasoning: New Evidence from Complex Network and Machine Learning[J]. Library and Information Service, 2024 , 68(11) : 99 -112 . DOI: 10.13266/j.issn.0252-3116.2024.11.009

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